setwd("~/Documents/Dropbox/Research/Adrian")
a<-read.csv ("AI_algo_2_humans.csv", header=T, sep=",")recommending movie
t.test(a$alg_1, a$ai_1)##
## Welch Two Sample t-test
##
## data: a$alg_1 and a$ai_1
## t = 1.4847, df = 804.85, p-value = 0.138
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.9328527 6.7249574
## sample estimates:
## mean of x mean of y
## 59.98718 57.09113t.test(a$humanqual_1, a$humanavg_1)##
## Welch Two Sample t-test
##
## data: a$humanqual_1 and a$humanavg_1
## t = 5.3463, df = 750.64, p-value = 1.192e-07
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 5.025505 10.857775
## sample estimates:
## mean of x mean of y
## 76.21205 68.27041t.test(a$alg_1, a$humanavg_1)##
## Welch Two Sample t-test
##
## data: a$alg_1 and a$humanavg_1
## t = -4.6065, df = 761.46, p-value = 4.798e-06
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -11.813149 -4.753308
## sample estimates:
## mean of x mean of y
## 59.98718 68.27041predicting joke funiness
t.test(a$alg_2, a$ai_2)##
## Welch Two Sample t-test
##
## data: a$alg_2 and a$ai_2
## t = 0.38127, df = 803.61, p-value = 0.7031
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2.925632 4.336146
## sample estimates:
## mean of x mean of y
## 30.55897 29.85372t.test(a$humanqual_2, a$humanavg_2)##
## Welch Two Sample t-test
##
## data: a$humanqual_2 and a$humanavg_2
## t = 6.2476, df = 800.14, p-value = 6.767e-10
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 7.527757 14.425132
## sample estimates:
## mean of x mean of y
## 64.79277 53.81633t.test(a$alg_2, a$humanavg_2)##
## Welch Two Sample t-test
##
## data: a$alg_2 and a$humanavg_2
## t = -12.719, df = 779.1, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -26.84695 -19.66775
## sample estimates:
## mean of x mean of y
## 30.55897 53.81633recommending restaurant
t.test(a$alg_3, a$ai_3)##
## Welch Two Sample t-test
##
## data: a$alg_3 and a$ai_3
## t = 1.2535, df = 803.57, p-value = 0.2104
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.331429 6.036392
## sample estimates:
## mean of x mean of y
## 61.51795 59.16547t.test(a$humanqual_3, a$humanavg_3)##
## Welch Two Sample t-test
##
## data: a$humanqual_3 and a$humanavg_3
## t = 5.5339, df = 741.95, p-value = 4.345e-08
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 5.13789 10.78739
## sample estimates:
## mean of x mean of y
## 78.37590 70.41327t.test(a$alg_3, a$humanavg_3)##
## Welch Two Sample t-test
##
## data: a$alg_3 and a$humanavg_3
## t = -5.0657, df = 761.77, p-value = 5.111e-07
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -12.342502 -5.448131
## sample estimates:
## mean of x mean of y
## 61.51795 70.41327recommending music
t.test(a$alg_4, a$ai_4)##
## Welch Two Sample t-test
##
## data: a$alg_4 and a$ai_4
## t = 0.34211, df = 800.27, p-value = 0.7324
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -3.171824 4.510802
## sample estimates:
## mean of x mean of y
## 59.37692 58.70743t.test(a$humanqual_4, a$humanavg_4)##
## Welch Two Sample t-test
##
## data: a$humanqual_4 and a$humanavg_4
## t = 6.7199, df = 751.07, p-value = 3.596e-11
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 7.346373 13.410131
## sample estimates:
## mean of x mean of y
## 74.94458 64.56633t.test(a$alg_4, a$humanavg_4)##
## Welch Two Sample t-test
##
## data: a$alg_4 and a$humanavg_4
## t = -2.7836, df = 762.37, p-value = 0.00551
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -8.849195 -1.529612
## sample estimates:
## mean of x mean of y
## 59.37692 64.56633gift
t.test(a$alg_5, a$ai_5)##
## Welch Two Sample t-test
##
## data: a$alg_5 and a$ai_5
## t = 0.090414, df = 802.56, p-value = 0.928
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -3.799768 4.166711
## sample estimates:
## mean of x mean of y
## 45.57436 45.39089t.test(a$humanqual_5, a$humanavg_5)##
## Welch Two Sample t-test
##
## data: a$humanqual_5 and a$humanavg_5
## t = 10.712, df = 741.47, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 14.44776 20.93158
## sample estimates:
## mean of x mean of y
## 75.28916 57.59949t.test(a$ai_5, a$humanavg_5)##
## Welch Two Sample t-test
##
## data: a$ai_5 and a$humanavg_5
## t = -6.3146, df = 805.23, p-value = 4.475e-10
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -16.003706 -8.413499
## sample estimates:
## mean of x mean of y
## 45.39089 57.59949romantic
t.test(a$alg_6, a$ai_6)##
## Welch Two Sample t-test
##
## data: a$alg_6 and a$ai_6
## t = 3.152, df = 793.49, p-value = 0.001682
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 2.274065 9.782308
## sample estimates:
## mean of x mean of y
## 37.28718 31.25899t.test(a$humanqual_6, a$humanavg_6)##
## Welch Two Sample t-test
##
## data: a$humanqual_6 and a$humanavg_6
## t = 9.7033, df = 780.08, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 14.39460 21.69579
## sample estimates:
## mean of x mean of y
## 58.88193 40.83673t.test(a$ai_6, a$humanavg_6)##
## Welch Two Sample t-test
##
## data: a$ai_6 and a$humanavg_6
## t = -5.0072, df = 795.89, p-value = 6.805e-07
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -13.332486 -5.822998
## sample estimates:
## mean of x mean of y
## 31.25899 40.83673t.test(a$alg_6, a$humanavg_6)##
## Welch Two Sample t-test
##
## data: a$alg_6 and a$humanavg_6
## t = -1.7801, df = 780, p-value = 0.07546
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -7.4639415 0.3648311
## sample estimates:
## mean of x mean of y
## 37.28718 40.83673news article
t.test(a$alg_7, a$ai_7)##
## Welch Two Sample t-test
##
## data: a$alg_7 and a$ai_7
## t = -1.0162, df = 802.2, p-value = 0.3099
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -6.005482 1.908525
## sample estimates:
## mean of x mean of y
## 37.25128 39.29976t.test(a$humanqual_7, a$humanavg_7)##
## Welch Two Sample t-test
##
## data: a$humanqual_7 and a$humanavg_7
## t = 16.274, df = 726.96, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 24.70146 31.47876
## sample estimates:
## mean of x mean of y
## 78.81205 50.72194t.test(a$ai_7, a$humanavg_7)##
## Welch Two Sample t-test
##
## data: a$ai_7 and a$humanavg_7
## t = -5.7727, df = 806.69, p-value = 1.112e-08
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -15.306065 -7.538292
## sample estimates:
## mean of x mean of y
## 39.29976 50.72194disease treatment
t.test(a$alg_8, a$ai_8)##
## Welch Two Sample t-test
##
## data: a$alg_8 and a$ai_8
## t = 1.6477, df = 804.81, p-value = 0.0998
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.6579838 7.5372680
## sample estimates:
## mean of x mean of y
## 47.53077 44.09113t.test(a$humanqual_8, a$humanavg_8)##
## Welch Two Sample t-test
##
## data: a$humanqual_8 and a$humanavg_8
## t = 27.423, df = 745.67, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 45.08724 52.04041
## sample estimates:
## mean of x mean of y
## 73.10602 24.54220t.test(a$ai_8, a$humanavg_8)##
## Welch Two Sample t-test
##
## data: a$ai_8 and a$humanavg_8
## t = 9.5638, df = 805.29, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 15.53662 23.56124
## sample estimates:
## mean of x mean of y
## 44.09113 24.54220t.test(a$alg_8, a$humanavg_8)##
## Welch Two Sample t-test
##
## data: a$alg_8 and a$humanavg_8
## t = 11.355, df = 777.42, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 19.01429 26.96285
## sample estimates:
## mean of x mean of y
## 47.53077 24.54220t.test(a$alg_8, a$humanqual_8)##
## Welch Two Sample t-test
##
## data: a$alg_8 and a$humanqual_8
## t = -14.045, df = 727.71, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -29.15013 -22.00038
## sample estimates:
## mean of x mean of y
## 47.53077 73.10602marketing strategy
t.test(a$alg_9, a$ai_9)##
## Welch Two Sample t-test
##
## data: a$alg_9 and a$ai_9
## t = 2.3803, df = 802.71, p-value = 0.01753
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.7874793 8.1944060
## sample estimates:
## mean of x mean of y
## 56.32308 51.83213t.test(a$humanqual_9, a$humanavg_9)##
## Welch Two Sample t-test
##
## data: a$humanqual_9 and a$humanavg_9
## t = 17.272, df = 747.31, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 26.57342 33.38859
## sample estimates:
## mean of x mean of y
## 69.9759 39.9949t.test(a$ai_9, a$humanavg_9)##
## Welch Two Sample t-test
##
## data: a$ai_9 and a$humanavg_9
## t = 6.2161, df = 803.19, p-value = 8.182e-10
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 8.099287 15.575186
## sample estimates:
## mean of x mean of y
## 51.83213 39.99490t.test(a$alg_9, a$humanavg_9)##
## Welch Two Sample t-test
##
## data: a$alg_9 and a$humanavg_9
## t = 8.492, df = 779.81, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 12.55376 20.10260
## sample estimates:
## mean of x mean of y
## 56.32308 39.99490t.test(a$alg_9, a$humanqual_9)##
## Welch Two Sample t-test
##
## data: a$alg_9 and a$humanqual_9
## t = -7.9539, df = 750.88, p-value = 6.645e-15
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -17.02251 -10.28314
## sample estimates:
## mean of x mean of y
## 56.32308 69.97590driving a car
t.test(a$alg_10, a$ai_10)##
## Welch Two Sample t-test
##
## data: a$alg_10 and a$ai_10
## t = -0.75988, df = 803.66, p-value = 0.4476
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -6.225904 2.750863
## sample estimates:
## mean of x mean of y
## 47.05385 48.79137t.test(a$humanqual_10, a$humanavg_10)##
## Welch Two Sample t-test
##
## data: a$humanqual_10 and a$humanavg_10
## t = 4.4707, df = 742.45, p-value = 9.015e-06
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 4.032019 10.345422
## sample estimates:
## mean of x mean of y
## 80.60964 73.42092t.test(a$ai_10, a$humanavg_10)##
## Welch Two Sample t-test
##
## data: a$ai_10 and a$humanavg_10
## t = -11.98, df = 776.75, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -28.66514 -20.59396
## sample estimates:
## mean of x mean of y
## 48.79137 73.42092driving a truck
t.test(a$alg_11, a$ai_11)##
## Welch Two Sample t-test
##
## data: a$alg_11 and a$ai_11
## t = -0.8571, df = 803.06, p-value = 0.3916
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -6.556038 2.570832
## sample estimates:
## mean of x mean of y
## 43.15128 45.14388t.test(a$humanqual_11, a$humanavg_11)##
## Welch Two Sample t-test
##
## data: a$humanqual_11 and a$humanavg_11
## t = 10.797, df = 689.56, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 15.42743 22.28535
## sample estimates:
## mean of x mean of y
## 80.66506 61.80867t.test(a$ai_11, a$humanavg_11)##
## Welch Two Sample t-test
##
## data: a$ai_11 and a$humanavg_11
## t = -7.634, df = 801.14, p-value = 6.477e-14
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -20.94980 -12.37977
## sample estimates:
## mean of x mean of y
## 45.14388 61.80867airplane
t.test(a$alg_12, a$ai_12)##
## Welch Two Sample t-test
##
## data: a$alg_12 and a$ai_12
## t = -0.3082, df = 803.43, p-value = 0.758
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -5.342047 3.892167
## sample estimates:
## mean of x mean of y
## 46.76667 47.49161t.test(a$humanqual_12, a$humanavg_12)##
## Welch Two Sample t-test
##
## data: a$humanqual_12 and a$humanavg_12
## t = 28.431, df = 718.15, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 49.15409 56.44632
## sample estimates:
## mean of x mean of y
## 79.27470 26.47449t.test(a$ai_12, a$humanavg_12)##
## Welch Two Sample t-test
##
## data: a$ai_12 and a$humanavg_12
## t = 9.3883, df = 803.98, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 16.62281 25.41142
## sample estimates:
## mean of x mean of y
## 47.49161 26.47449t.test(a$alg_12, a$humanqual_12)##
## Welch Two Sample t-test
##
## data: a$alg_12 and a$humanqual_12
## t = -16.315, df = 672.87, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -36.42033 -28.59574
## sample estimates:
## mean of x mean of y
## 46.76667 79.27470driving a subway
t.test(a$alg_13, a$ai_13)##
## Welch Two Sample t-test
##
## data: a$alg_13 and a$ai_13
## t = -0.76967, df = 801.4, p-value = 0.4417
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -6.545779 2.858378
## sample estimates:
## mean of x mean of y
## 52.04359 53.88729t.test(a$humanqual_13, a$humanavg_13)##
## Welch Two Sample t-test
##
## data: a$humanqual_13 and a$humanavg_13
## t = 21.351, df = 722.19, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 38.40940 46.18825
## sample estimates:
## mean of x mean of y
## 76.51566 34.21684t.test(a$ai_13, a$humanavg_13)##
## Welch Two Sample t-test
##
## data: a$ai_13 and a$humanavg_13
## t = 8.5093, df = 806.96, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 15.13294 24.20797
## sample estimates:
## mean of x mean of y
## 53.88729 34.21684t.test(a$alg_13, a$humanqual_13)##
## Welch Two Sample t-test
##
## data: a$alg_13 and a$humanqual_13
## t = -11.775, df = 690.2, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -28.55261 -20.39154
## sample estimates:
## mean of x mean of y
## 52.04359 76.51566predicting weather
t.test(a$alg_14, a$ai_14)##
## Welch Two Sample t-test
##
## data: a$alg_14 and a$ai_14
## t = 0.5765, df = 801, p-value = 0.5644
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2.367761 4.336844
## sample estimates:
## mean of x mean of y
## 68.68718 67.70264t.test(a$humanqual_14, a$humanavg_14)##
## Welch Two Sample t-test
##
## data: a$humanqual_14 and a$humanavg_14
## t = 9.633, df = 795.34, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 13.47889 20.37800
## sample estimates:
## mean of x mean of y
## 57.08916 40.16071t.test(a$ai_14, a$humanavg_14)##
## Welch Two Sample t-test
##
## data: a$ai_14 and a$humanavg_14
## t = 15.708, df = 796.01, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 24.10014 30.98371
## sample estimates:
## mean of x mean of y
## 67.70264 40.16071t.test(a$alg_14, a$humanqual_14)##
## Welch Two Sample t-test
##
## data: a$alg_14 and a$humanqual_14
## t = 6.7751, df = 799.8, p-value = 2.411e-11
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 8.23774 14.95831
## sample estimates:
## mean of x mean of y
## 68.68718 57.08916predicting stock market
t.test(a$alg_15, a$ai_15)##
## Welch Two Sample t-test
##
## data: a$alg_15 and a$ai_15
## t = 2.0661, df = 804.91, p-value = 0.03914
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.1872905 7.3150523
## sample estimates:
## mean of x mean of y
## 62.63846 58.88729t.test(a$humanqual_15, a$humanavg_15)##
## Welch Two Sample t-test
##
## data: a$humanqual_15 and a$humanavg_15
## t = 12.531, df = 796.17, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 18.05357 24.75996
## sample estimates:
## mean of x mean of y
## 54.53976 33.13299t.test(a$alg_15, a$humanavg_15)##
## Welch Two Sample t-test
##
## data: a$alg_15 and a$humanavg_15
## t = 16.57, df = 778.77, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 26.00993 33.00101
## sample estimates:
## mean of x mean of y
## 62.63846 33.13299t.test(a$alg_15, a$humanqual_15)##
## Welch Two Sample t-test
##
## data: a$alg_15 and a$humanqual_15
## t = 4.7003, df = 792.28, p-value = 3.062e-06
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 4.71651 11.48089
## sample estimates:
## mean of x mean of y
## 62.63846 54.53976predicting election
t.test(a$alg_16, a$ai_16)##
## Welch Two Sample t-test
##
## data: a$alg_16 and a$ai_16
## t = 1.0863, df = 801.49, p-value = 0.2777
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.678377 5.838052
## sample estimates:
## mean of x mean of y
## 54.15897 52.07914t.test(a$humanqual_16, a$humanavg_16)##
## Welch Two Sample t-test
##
## data: a$humanqual_16 and a$humanavg_16
## t = 9.0754, df = 803.54, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 11.83466 18.36699
## sample estimates:
## mean of x mean of y
## 51.41205 36.31122t.test(a$alg_16, a$humanavg_16)##
## Welch Two Sample t-test
##
## data: a$alg_16 and a$humanavg_16
## t = 9.8301, df = 762.64, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 14.28355 21.41195
## sample estimates:
## mean of x mean of y
## 54.15897 36.31122t.test(a$alg_16, a$humanqual_16)##
## Welch Two Sample t-test
##
## data: a$alg_16 and a$humanqual_16
## t = 1.522, df = 774.09, p-value = 0.1284
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.7960694 6.2899218
## sample estimates:
## mean of x mean of y
## 54.15897 51.41205predicting employee performance
t.test(a$alg_17, a$ai_17)##
## Welch Two Sample t-test
##
## data: a$alg_17 and a$ai_17
## t = 1.2786, df = 803.66, p-value = 0.2014
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.288217 6.101647
## sample estimates:
## mean of x mean of y
## 50.46667 48.05995t.test(a$humanqual_17, a$humanavg_17)##
## Welch Two Sample t-test
##
## data: a$humanqual_17 and a$humanavg_17
## t = 11.155, df = 784.06, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 15.40256 21.98109
## sample estimates:
## mean of x mean of y
## 60.51325 41.82143t.test(a$alg_17, a$humanavg_17)##
## Welch Two Sample t-test
##
## data: a$alg_17 and a$humanavg_17
## t = 4.7041, df = 777.28, p-value = 3.017e-06
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 5.037598 12.252879
## sample estimates:
## mean of x mean of y
## 50.46667 41.82143t.test(a$alg_17, a$humanqual_17)##
## Welch Two Sample t-test
##
## data: a$alg_17 and a$humanqual_17
## t = -5.8013, df = 765.54, p-value = 9.629e-09
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -13.446196 -6.646977
## sample estimates:
## mean of x mean of y
## 50.46667 60.51325predicting student performance
t.test(a$alg_18, a$ai_18)##
## Welch Two Sample t-test
##
## data: a$alg_18 and a$ai_18
## t = 0.52114, df = 804.8, p-value = 0.6024
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2.675954 4.610431
## sample estimates:
## mean of x mean of y
## 46.27179 45.30456t.test(a$humanqual_18, a$humanavg_18)##
## Welch Two Sample t-test
##
## data: a$humanqual_18 and a$humanavg_18
## t = 12.893, df = 769.57, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 18.41684 25.03236
## sample estimates:
## mean of x mean of y
## 62.78072 41.05612t.test(a$alg_18, a$humanavg_18)##
## Welch Two Sample t-test
##
## data: a$alg_18 and a$humanavg_18
## t = 2.839, df = 779.98, p-value = 0.004644
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 1.609269 8.822075
## sample estimates:
## mean of x mean of y
## 46.27179 41.05612t.test(a$alg_18, a$humanqual_18)##
## Welch Two Sample t-test
##
## data: a$alg_18 and a$humanqual_18
## t = -9.7835, df = 766.1, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -19.82145 -13.19641
## sample estimates:
## mean of x mean of y
## 46.27179 62.78072predicting recidivism
t.test(a$alg_19, a$ai_19)##
## Welch Two Sample t-test
##
## data: a$alg_19 and a$ai_19
## t = 1.6443, df = 791.82, p-value = 0.1005
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.6149504 6.9604217
## sample estimates:
## mean of x mean of y
## 41.96410 38.79137t.test(a$humanqual_19, a$humanavg_19)##
## Welch Two Sample t-test
##
## data: a$humanqual_19 and a$humanavg_19
## t = 10.304, df = 794.89, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 14.67524 21.58215
## sample estimates:
## mean of x mean of y
## 54.06747 35.93878t.test(a$alg_19, a$humanavg_19)##
## Welch Two Sample t-test
##
## data: a$alg_19 and a$humanavg_19
## t = 3.1252, df = 772.31, p-value = 0.001843
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 2.240635 9.810020
## sample estimates:
## mean of x mean of y
## 41.96410 35.93878t.test(a$alg_19, a$humanqual_19)##
## Welch Two Sample t-test
##
## data: a$alg_19 and a$humanqual_19
## t = -6.51, df = 768.73, p-value = 1.356e-10
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -15.753092 -8.453643
## sample estimates:
## mean of x mean of y
## 41.96410 54.06747buy sell stocks
t.test(a$alg_20, a$ai_20)##
## Welch Two Sample t-test
##
## data: a$alg_20 and a$ai_20
## t = 2.2254, df = 804.7, p-value = 0.02633
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.5033584 8.0328526
## sample estimates:
## mean of x mean of y
## 60.06667 55.79856t.test(a$humanqual_20, a$humanavg_20)##
## Welch Two Sample t-test
##
## data: a$humanqual_20 and a$humanavg_20
## t = 14.653, df = 771.28, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 21.87744 28.64604
## sample estimates:
## mean of x mean of y
## 61.84337 36.58163t.test(a$alg_20, a$humanavg_20)##
## Welch Two Sample t-test
##
## data: a$alg_20 and a$humanavg_20
## t = 12.434, df = 779.72, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 19.77741 27.19266
## sample estimates:
## mean of x mean of y
## 60.06667 36.58163t.test(a$alg_20, a$humanqual_20)##
## Welch Two Sample t-test
##
## data: a$alg_20 and a$humanqual_20
## t = -1.0205, df = 763.45, p-value = 0.3078
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -5.194439 1.641025
## sample estimates:
## mean of x mean of y
## 60.06667 61.84337diagnose disease
t.test(a$alg_21, a$ai_21)##
## Welch Two Sample t-test
##
## data: a$alg_21 and a$ai_21
## t = -0.043616, df = 804.72, p-value = 0.9652
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -4.154042 3.973448
## sample estimates:
## mean of x mean of y
## 48.34615 48.43645t.test(a$humanqual_21, a$humanavg_21)##
## Welch Two Sample t-test
##
## data: a$humanqual_21 and a$humanavg_21
## t = 27.38, df = 738.61, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 44.82092 51.74468
## sample estimates:
## mean of x mean of y
## 73.44096 25.15816t.test(a$ai_21, a$humanavg_21)##
## Welch Two Sample t-test
##
## data: a$ai_21 and a$humanavg_21
## t = 11.425, df = 806.77, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 19.27904 27.27754
## sample estimates:
## mean of x mean of y
## 48.43645 25.15816t.test(a$alg_21, a$humanqual_21)##
## Welch Two Sample t-test
##
## data: a$alg_21 and a$humanqual_21
## t = -13.932, df = 722.96, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -28.63113 -21.55849
## sample estimates:
## mean of x mean of y
## 48.34615 73.44096hire fire employees
t.test(a$alg_22, a$ai_22)##
## Welch Two Sample t-test
##
## data: a$alg_22 and a$ai_22
## t = 0.75659, df = 801.96, p-value = 0.4495
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2.250076 5.072507
## sample estimates:
## mean of x mean of y
## 33.94359 32.53237t.test(a$humanqual_22, a$humanavg_22)##
## Welch Two Sample t-test
##
## data: a$humanqual_22 and a$humanavg_22
## t = 16.703, df = 728.57, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 25.87093 32.76234
## sample estimates:
## mean of x mean of y
## 72.16867 42.85204t.test(a$ai_22, a$humanavg_22)##
## Welch Two Sample t-test
##
## data: a$ai_22 and a$humanavg_22
## t = -5.3753, df = 796.73, p-value = 1.005e-07
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -14.088177 -6.551156
## sample estimates:
## mean of x mean of y
## 32.53237 42.85204t.test(a$alg_22, a$humanqual_22)##
## Welch Two Sample t-test
##
## data: a$alg_22 and a$humanqual_22
## t = -22.549, df = 746.25, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -41.55306 -34.89711
## sample estimates:
## mean of x mean of y
## 33.94359 72.16867giving directions
t.test(a$alg_23, a$ai_23)##
## Welch Two Sample t-test
##
## data: a$alg_23 and a$ai_23
## t = -0.08318, df = 802.28, p-value = 0.9337
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -3.021148 2.775510
## sample estimates:
## mean of x mean of y
## 82.25128 82.37410t.test(a$humanqual_23, a$humanavg_23)##
## Welch Two Sample t-test
##
## data: a$humanqual_23 and a$humanavg_23
## t = 3.0604, df = 801.59, p-value = 0.002284
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 1.828955 8.371573
## sample estimates:
## mean of x mean of y
## 70.46506 65.36480t.test(a$ai_23, a$humanavg_23)##
## Welch Two Sample t-test
##
## data: a$ai_23 and a$humanavg_23
## t = 10.752, df = 781.28, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 13.90381 20.11480
## sample estimates:
## mean of x mean of y
## 82.3741 65.3648t.test(a$alg_23, a$humanqual_23)##
## Welch Two Sample t-test
##
## data: a$alg_23 and a$humanqual_23
## t = 7.5226, df = 800.23, p-value = 1.444e-13
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 8.710759 14.861685
## sample estimates:
## mean of x mean of y
## 82.25128 70.46506play piano
t.test(a$alg_24, a$ai_24)##
## Welch Two Sample t-test
##
## data: a$alg_24 and a$ai_24
## t = -2.0446, df = 793.27, p-value = 0.04123
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -9.190799 -0.187176
## sample estimates:
## mean of x mean of y
## 61.02564 65.71463t.test(a$humanqual_24, a$humanavg_24)##
## Welch Two Sample t-test
##
## data: a$humanqual_24 and a$humanavg_24
## t = 16.904, df = 668.68, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 27.38546 34.58351
## sample estimates:
## mean of x mean of y
## 83.52530 52.54082t.test(a$ai_24, a$humanavg_24)##
## Welch Two Sample t-test
##
## data: a$ai_24 and a$humanavg_24
## t = 6.0203, df = 806.31, p-value = 2.643e-09
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 8.878487 17.469137
## sample estimates:
## mean of x mean of y
## 65.71463 52.54082t.test(a$alg_24, a$humanqual_24)##
## Welch Two Sample t-test
##
## data: a$alg_24 and a$humanqual_24
## t = -11.495, df = 629.42, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -26.34322 -18.65610
## sample estimates:
## mean of x mean of y
## 61.02564 83.52530compose song
t.test(a$alg_25, a$ai_25)##
## Welch Two Sample t-test
##
## data: a$alg_25 and a$ai_25
## t = 1.0895, df = 802.02, p-value = 0.2763
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.843405 6.441819
## sample estimates:
## mean of x mean of y
## 43.43590 41.13669t.test(a$humanqual_25, a$humanavg_25)##
## Welch Two Sample t-test
##
## data: a$humanqual_25 and a$humanavg_25
## t = 19.476, df = 707.37, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 32.33482 39.58500
## sample estimates:
## mean of x mean of y
## 81.11807 45.15816t.test(a$ai_25, a$humanavg_25)##
## Welch Two Sample t-test
##
## data: a$ai_25 and a$humanavg_25
## t = -1.9053, df = 804.24, p-value = 0.0571
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -8.1645729 0.1216277
## sample estimates:
## mean of x mean of y
## 41.13669 45.15816t.test(a$alg_25, a$humanqual_25)##
## Welch Two Sample t-test
##
## data: a$alg_25 and a$humanqual_25
## t = -20.412, df = 704.51, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -41.30671 -34.05764
## sample estimates:
## mean of x mean of y
## 43.43590 81.11807schedule events
t.test(a$alg_26, a$ai_26)##
## Welch Two Sample t-test
##
## data: a$alg_26 and a$ai_26
## t = -0.49797, df = 805, p-value = 0.6186
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -4.486323 2.670681
## sample estimates:
## mean of x mean of y
## 68.57179 69.47962t.test(a$humanqual_26, a$humanavg_26)##
## Welch Two Sample t-test
##
## data: a$humanqual_26 and a$humanavg_26
## t = 11.933, df = 713.62, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 15.67543 21.84921
## sample estimates:
## mean of x mean of y
## 77.92048 59.15816t.test(a$ai_26, a$humanavg_26)##
## Welch Two Sample t-test
##
## data: a$ai_26 and a$humanavg_26
## t = 5.6297, df = 806.94, p-value = 2.492e-08
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 6.722672 13.920234
## sample estimates:
## mean of x mean of y
## 69.47962 59.15816t.test(a$alg_26, a$humanqual_26)##
## Welch Two Sample t-test
##
## data: a$alg_26 and a$humanqual_26
## t = -5.9919, df = 715.37, p-value = 3.287e-09
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -12.411870 -6.285504
## sample estimates:
## mean of x mean of y
## 68.57179 77.92048analyze data
t.test(a$alg_27, a$ai_27)##
## Welch Two Sample t-test
##
## data: a$alg_27 and a$ai_27
## t = 0.69587, df = 804.4, p-value = 0.4867
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.920670 4.030354
## sample estimates:
## mean of x mean of y
## 80.40256 79.34772t.test(a$humanqual_27, a$humanavg_27)##
## Welch Two Sample t-test
##
## data: a$humanqual_27 and a$humanavg_27
## t = 14.528, df = 755.14, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 21.66789 28.43838
## sample estimates:
## mean of x mean of y
## 68.92048 43.86735t.test(a$ai_27, a$humanavg_27)##
## Welch Two Sample t-test
##
## data: a$ai_27 and a$humanavg_27
## t = 20.332, df = 766.27, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 32.05476 38.90599
## sample estimates:
## mean of x mean of y
## 79.34772 43.86735t.test(a$alg_27, a$humanqual_27)##
## Welch Two Sample t-test
##
## data: a$alg_27 and a$humanqual_27
## t = 7.6953, df = 803, p-value = 4.142e-14
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 8.553214 14.410951
## sample estimates:
## mean of x mean of y
## 80.40256 68.92048First select the cases of each condition in which that condition was seen first
hum <- a[c(69:95, 129)]
table(hum$DO.BR.FL_5)##
## ai + algorithm|humans humans|ai + algorithm
## 425 382humfirst <- subset(hum, DO.BR.FL_5 == "humans|ai + algorithm")
humfirst <- subset(humfirst, humanqual_1 != "NA")
humavg <- a[c(96:122, 129)]
humavgfirst <- subset(humavg, DO.BR.FL_5 == "humans|ai + algorithm")
humavgfirst <- subset(humavgfirst, humanavg_1 != "NA")
names(a)## [1] "v1" "V2" "V3"
## [4] "V4" "V5" "V6"
## [7] "V7" "V8" "V9"
## [10] "V10" "Q11" "Q13"
## [13] "Q15" "attn" "alg_1"
## [16] "alg_2" "alg_3" "alg_4"
## [19] "alg_5" "alg_6" "alg_7"
## [22] "alg_8" "alg_9" "alg_10"
## [25] "alg_11" "alg_12" "alg_13"
## [28] "alg_14" "alg_15" "alg_16"
## [31] "alg_17" "alg_18" "alg_19"
## [34] "alg_20" "alg_21" "alg_22"
## [37] "alg_23" "alg_24" "alg_25"
## [40] "alg_26" "alg_27" "ai_1"
## [43] "ai_2" "ai_3" "ai_4"
## [46] "ai_5" "ai_6" "ai_7"
## [49] "ai_8" "ai_9" "ai_10"
## [52] "ai_11" "ai_12" "ai_13"
## [55] "ai_14" "ai_15" "ai_16"
## [58] "ai_17" "ai_18" "ai_19"
## [61] "ai_20" "ai_21" "ai_22"
## [64] "ai_23" "ai_24" "ai_25"
## [67] "ai_26" "ai_27" "humanqual_1"
## [70] "humanqual_2" "humanqual_3" "humanqual_4"
## [73] "humanqual_5" "humanqual_6" "humanqual_7"
## [76] "humanqual_8" "humanqual_9" "humanqual_10"
## [79] "humanqual_11" "humanqual_12" "humanqual_13"
## [82] "humanqual_14" "humanqual_15" "humanqual_16"
## [85] "humanqual_17" "humanqual_18" "humanqual_19"
## [88] "humanqual_20" "humanqual_21" "humanqual_22"
## [91] "humanqual_23" "humanqual_24" "humanqual_25"
## [94] "humanqual_26" "humanqual_27" "humanavg_1"
## [97] "humanavg_2" "humanavg_3" "humanavg_4"
## [100] "humanavg_5" "humanavg_6" "humanavg_7"
## [103] "humanavg_8" "humanavg_9" "humanavg_10"
## [106] "humanavg_11" "humanavg_12" "humanavg_13"
## [109] "humanavg_14" "humanavg_15" "humanavg_16"
## [112] "humanavg_17" "humanavg_18" "humanavg_19"
## [115] "humanavg_20" "humanavg_21" "humanavg_22"
## [118] "humanavg_23" "humanavg_24" "humanavg_25"
## [121] "humanavg_26" "humanavg_27" "humandiff"
## [124] "compdiff" "Q4" "Q6"
## [127] "Q8_1" "Q8_2" "DO.BR.FL_5"
## [130] "DO.BL.ai.algorithm" "DO.BL.humans" "DO.Q.alg"
## [133] "DO.Q.ai" "DO.Q.humanavg" "DO.Q.humanqual"
## [136] "LocationLatitude" "LocationLongitude" "LocationAccuracy"ai <- a[c(42:68, 129)]
aifirst <- subset(ai, DO.BR.FL_5 == "ai + algorithm|humans")
aifirst <- subset(aifirst, ai_1 != "NA")
alg <- a[c(15:41, 129)]
alg <- subset(alg, alg_1 != "NA")
algfirst <- subset(alg, DO.BR.FL_5 == "ai + algorithm|humans")recommending movie
t.test(algfirst$alg_1, aifirst$ai_1)##
## Welch Two Sample t-test
##
## data: algfirst$alg_1 and aifirst$ai_1
## t = 0.86526, df = 418.74, p-value = 0.3874
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2.838903 7.303494
## sample estimates:
## mean of x mean of y
## 63.75248 61.52018t.test(humfirst$humanqual_1, humavgfirst$humanavg_1)##
## Welch Two Sample t-test
##
## data: humfirst$humanqual_1 and humavgfirst$humanavg_1
## t = 4.7097, df = 365.67, p-value = 3.529e-06
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 5.987937 14.572809
## sample estimates:
## mean of x mean of y
## 74.91579 64.63542t.test(algfirst$alg_1, humfirst$humanqual_1)##
## Welch Two Sample t-test
##
## data: algfirst$alg_1 and humfirst$humanqual_1
## t = -4.8057, df = 364.26, p-value = 2.259e-06
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -15.731373 -6.595255
## sample estimates:
## mean of x mean of y
## 63.75248 74.91579t.test(algfirst$alg_1, humavgfirst$humanavg_1)##
## Welch Two Sample t-test
##
## data: algfirst$alg_1 and humavgfirst$humanavg_1
## t = -0.34999, df = 389.88, p-value = 0.7265
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -5.842850 4.076967
## sample estimates:
## mean of x mean of y
## 63.75248 64.63542t.test(a$alg_1, a$humanavg_1)##
## Welch Two Sample t-test
##
## data: a$alg_1 and a$humanavg_1
## t = -4.6065, df = 761.46, p-value = 4.798e-06
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -11.813149 -4.753308
## sample estimates:
## mean of x mean of y
## 59.98718 68.27041predicting joke funiness
t.test(algfirst$alg_2, aifirst$ai_2)##
## Welch Two Sample t-test
##
## data: algfirst$alg_2 and aifirst$ai_2
## t = -0.074905, df = 420.75, p-value = 0.9403
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -5.183919 4.803330
## sample estimates:
## mean of x mean of y
## 31.45545 31.64574t.test(humfirst$humanqual_2, humavgfirst$humanavg_2)##
## Welch Two Sample t-test
##
## data: humfirst$humanqual_2 and humavgfirst$humanavg_2
## t = 4.7353, df = 379.99, p-value = 3.094e-06
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 7.111815 17.211650
## sample estimates:
## mean of x mean of y
## 63.02632 50.86458t.test(algfirst$alg_2, humavgfirst$humanavg_2)##
## Welch Two Sample t-test
##
## data: algfirst$alg_2 and humavgfirst$humanavg_2
## t = -7.5378, df = 391.66, p-value = 3.353e-13
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -24.47152 -14.34676
## sample estimates:
## mean of x mean of y
## 31.45545 50.86458recommending restaurant
t.test(algfirst$alg_3, aifirst$ai_3)##
## Welch Two Sample t-test
##
## data: algfirst$alg_3 and aifirst$ai_3
## t = 0.9673, df = 420.2, p-value = 0.334
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2.393571 7.031941
## sample estimates:
## mean of x mean of y
## 65.72277 63.40359t.test(humfirst$humanqual_3, humavgfirst$humanavg_3)##
## Welch Two Sample t-test
##
## data: humfirst$humanqual_3 and humavgfirst$humanavg_3
## t = 4.93, df = 365.04, p-value = 1.251e-06
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 6.238613 14.518185
## sample estimates:
## mean of x mean of y
## 77.32632 66.94792t.test(algfirst$alg_3, humfirst$humanqual_3)##
## Welch Two Sample t-test
##
## data: algfirst$alg_3 and humfirst$humanqual_3
## t = -5.3401, df = 371.1, p-value = 1.623e-07
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -15.876322 -7.330765
## sample estimates:
## mean of x mean of y
## 65.72277 77.32632t.test(algfirst$alg_3, humavgfirst$humanavg_3)##
## Welch Two Sample t-test
##
## data: algfirst$alg_3 and humavgfirst$humanavg_3
## t = -0.51578, df = 391.74, p-value = 0.6063
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -5.895102 3.444813
## sample estimates:
## mean of x mean of y
## 65.72277 66.94792t.test(a$alg_3, a$humanavg_3)##
## Welch Two Sample t-test
##
## data: a$alg_3 and a$humanavg_3
## t = -5.0657, df = 761.77, p-value = 5.111e-07
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -12.342502 -5.448131
## sample estimates:
## mean of x mean of y
## 61.51795 70.41327recommending music
t.test(algfirst$alg_4, aifirst$ai_4)##
## Welch Two Sample t-test
##
## data: algfirst$alg_4 and aifirst$ai_4
## t = -0.16773, df = 413.4, p-value = 0.8669
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -5.497944 4.633450
## sample estimates:
## mean of x mean of y
## 62.88614 63.31839t.test(humfirst$humanqual_4, humavgfirst$humanavg_4)##
## Welch Two Sample t-test
##
## data: humfirst$humanqual_4 and humavgfirst$humanavg_4
## t = 5.5604, df = 374.27, p-value = 5.128e-08
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 8.242321 17.261078
## sample estimates:
## mean of x mean of y
## 73.66316 60.91146t.test(algfirst$alg_4, humfirst$humanqual_4)##
## Welch Two Sample t-test
##
## data: algfirst$alg_4 and humfirst$humanqual_4
## t = -4.4176, df = 375.05, p-value = 1.308e-05
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -15.573976 -5.980062
## sample estimates:
## mean of x mean of y
## 62.88614 73.66316t.test(algfirst$alg_4, humavgfirst$humanavg_4)##
## Welch Two Sample t-test
##
## data: algfirst$alg_4 and humavgfirst$humanavg_4
## t = 0.76691, df = 389.57, p-value = 0.4436
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -3.08766 7.03702
## sample estimates:
## mean of x mean of y
## 62.88614 60.91146t.test(a$alg_4, a$humanavg_4)##
## Welch Two Sample t-test
##
## data: a$alg_4 and a$humanavg_4
## t = -2.7836, df = 762.37, p-value = 0.00551
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -8.849195 -1.529612
## sample estimates:
## mean of x mean of y
## 59.37692 64.56633gift
t.test(algfirst$alg_5, aifirst$ai_5)##
## Welch Two Sample t-test
##
## data: algfirst$alg_5 and aifirst$ai_5
## t = 0.09132, df = 420.58, p-value = 0.9273
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -5.201962 5.708866
## sample estimates:
## mean of x mean of y
## 50.02475 49.77130t.test(humfirst$humanqual_5, humavgfirst$humanavg_5)##
## Welch Two Sample t-test
##
## data: humfirst$humanqual_5 and humavgfirst$humanavg_5
## t = 9.2411, df = 355.4, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 17.60002 27.11642
## sample estimates:
## mean of x mean of y
## 75.38947 53.03125t.test(aifirst$ai_5, humfirst$humanqual_5)##
## Welch Two Sample t-test
##
## data: aifirst$ai_5 and humfirst$humanqual_5
## t = -10.554, df = 396.17, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -30.39025 -20.84610
## sample estimates:
## mean of x mean of y
## 49.77130 75.38947t.test(aifirst$ai_5, humavgfirst$humanavg_5)##
## Welch Two Sample t-test
##
## data: aifirst$ai_5 and humavgfirst$humanavg_5
## t = -1.1933, df = 410.98, p-value = 0.2334
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -8.630073 2.110174
## sample estimates:
## mean of x mean of y
## 49.77130 53.03125t.test(a$ai_5, a$humanavg_5)##
## Welch Two Sample t-test
##
## data: a$ai_5 and a$humanavg_5
## t = -6.3146, df = 805.23, p-value = 4.475e-10
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -16.003706 -8.413499
## sample estimates:
## mean of x mean of y
## 45.39089 57.59949romantic
t.test(algfirst$alg_6, aifirst$ai_6)##
## Welch Two Sample t-test
##
## data: algfirst$alg_6 and aifirst$ai_6
## t = 2.4404, df = 414.22, p-value = 0.01509
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 1.227215 11.390109
## sample estimates:
## mean of x mean of y
## 39.03960 32.73094t.test(humfirst$humanqual_6, humavgfirst$humanavg_6)##
## Welch Two Sample t-test
##
## data: humfirst$humanqual_6 and humavgfirst$humanavg_6
## t = 6.918, df = 376.01, p-value = 1.98e-11
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 13.71810 24.61282
## sample estimates:
## mean of x mean of y
## 57.88421 38.71875t.test(aifirst$ai_6, humavgfirst$humanavg_6)##
## Welch Two Sample t-test
##
## data: aifirst$ai_6 and humavgfirst$humanavg_6
## t = -2.2201, df = 389.6, p-value = 0.02699
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -11.2905004 -0.6851161
## sample estimates:
## mean of x mean of y
## 32.73094 38.71875t.test(algfirst$alg_6, humfirst$humanqual_6)##
## Welch Two Sample t-test
##
## data: algfirst$alg_6 and humfirst$humanqual_6
## t = -7.0807, df = 390, p-value = 6.725e-12
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -24.07710 -13.61211
## sample estimates:
## mean of x mean of y
## 39.03960 57.88421t.test(algfirst$alg_6, humavgfirst$humanavg_6)##
## Welch Two Sample t-test
##
## data: algfirst$alg_6 and humavgfirst$humanavg_6
## t = 0.11405, df = 388.09, p-value = 0.9093
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -5.210190 5.851898
## sample estimates:
## mean of x mean of y
## 39.03960 38.71875t.test(a$alg_6, a$humanavg_6)##
## Welch Two Sample t-test
##
## data: a$alg_6 and a$humanavg_6
## t = -1.7801, df = 780, p-value = 0.07546
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -7.4639415 0.3648311
## sample estimates:
## mean of x mean of y
## 37.28718 40.83673news article
t.test(algfirst$alg_7, aifirst$ai_7)##
## Welch Two Sample t-test
##
## data: algfirst$alg_7 and aifirst$ai_7
## t = -0.89392, df = 419.26, p-value = 0.3719
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -7.692826 2.883165
## sample estimates:
## mean of x mean of y
## 36.65347 39.05830t.test(humfirst$humanqual_7, humavgfirst$humanavg_7)##
## Welch Two Sample t-test
##
## data: humfirst$humanqual_7 and humavgfirst$humanavg_7
## t = 13.476, df = 360.78, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 28.03651 37.61755
## sample estimates:
## mean of x mean of y
## 76.89474 44.06771t.test(aifirst$ai_7, humavgfirst$humanavg_7)##
## Welch Two Sample t-test
##
## data: aifirst$ai_7 and humavgfirst$humanavg_7
## t = -1.8767, df = 408.51, p-value = 0.06127
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -10.2564869 0.2376621
## sample estimates:
## mean of x mean of y
## 39.05830 44.06771disease treatment
t.test(algfirst$alg_8, aifirst$ai_8)##
## Welch Two Sample t-test
##
## data: algfirst$alg_8 and aifirst$ai_8
## t = 1.6531, df = 420.56, p-value = 0.09905
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.873384 10.114116
## sample estimates:
## mean of x mean of y
## 46.30198 41.68161t.test(humfirst$humanqual_8, humavgfirst$humanavg_8)##
## Welch Two Sample t-test
##
## data: humfirst$humanqual_8 and humavgfirst$humanavg_8
## t = 17.249, df = 365.23, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 41.76487 52.51311
## sample estimates:
## mean of x mean of y
## 71.54737 24.40838t.test(aifirst$ai_8, humavgfirst$humanavg_8)##
## Welch Two Sample t-test
##
## data: aifirst$ai_8 and humavgfirst$humanavg_8
## t = 6.006, df = 401.87, p-value = 4.261e-09
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 11.61932 22.92715
## sample estimates:
## mean of x mean of y
## 41.68161 24.40838t.test(algfirst$alg_8, humavgfirst$humanavg_8)##
## Welch Two Sample t-test
##
## data: algfirst$alg_8 and humavgfirst$humanavg_8
## t = 7.519, df = 388.36, p-value = 3.86e-13
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 16.16878 27.61842
## sample estimates:
## mean of x mean of y
## 46.30198 24.40838t.test(algfirst$alg_8, humfirst$humanqual_8)##
## Welch Two Sample t-test
##
## data: algfirst$alg_8 and humfirst$humanqual_8
## t = -9.536, df = 385.05, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -30.45052 -20.04025
## sample estimates:
## mean of x mean of y
## 46.30198 71.54737marketing strategy
t.test(algfirst$alg_9, aifirst$ai_9)##
## Welch Two Sample t-test
##
## data: algfirst$alg_9 and aifirst$ai_9
## t = 1.8367, df = 419.84, p-value = 0.06696
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.323578 9.544197
## sample estimates:
## mean of x mean of y
## 57.62376 53.01345t.test(humfirst$humanqual_9, humavgfirst$humanavg_9)##
## Welch Two Sample t-test
##
## data: humfirst$humanqual_9 and humavgfirst$humanavg_9
## t = 12.786, df = 361.56, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 27.36529 37.31300
## sample estimates:
## mean of x mean of y
## 69.05789 36.71875t.test(aifirst$ai_9, humavgfirst$humanavg_9)##
## Welch Two Sample t-test
##
## data: aifirst$ai_9 and humavgfirst$humanavg_9
## t = 6.1714, df = 396.3, p-value = 1.674e-09
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 11.10384 21.48557
## sample estimates:
## mean of x mean of y
## 53.01345 36.71875t.test(algfirst$alg_9, humavgfirst$humanavg_9)##
## Welch Two Sample t-test
##
## data: algfirst$alg_9 and humavgfirst$humanavg_9
## t = 7.7885, df = 386.55, p-value = 6.3e-14
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 15.62780 26.18222
## sample estimates:
## mean of x mean of y
## 57.62376 36.71875t.test(algfirst$alg_9, humfirst$humanqual_9)##
## Welch Two Sample t-test
##
## data: algfirst$alg_9 and humfirst$humanqual_9
## t = -4.7783, df = 385.32, p-value = 2.518e-06
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -16.138922 -6.729343
## sample estimates:
## mean of x mean of y
## 57.62376 69.05789driving a car
t.test(algfirst$alg_10, aifirst$ai_10)##
## Welch Two Sample t-test
##
## data: algfirst$alg_10 and aifirst$ai_10
## t = -0.33973, df = 421.53, p-value = 0.7342
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -7.220777 5.092552
## sample estimates:
## mean of x mean of y
## 46.12871 47.19283t.test(humfirst$humanqual_10, humavgfirst$humanavg_10)##
## Welch Two Sample t-test
##
## data: humfirst$humanqual_10 and humavgfirst$humanavg_10
## t = 4.8735, df = 358.81, p-value = 1.649e-06
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 6.878091 16.184519
## sample estimates:
## mean of x mean of y
## 80.50526 68.97396t.test(aifirst$ai_10, humavgfirst$humanavg_10)##
## Welch Two Sample t-test
##
## data: aifirst$ai_10 and humavgfirst$humanavg_10
## t = -7.5361, df = 409.78, p-value = 3.133e-13
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -27.46266 -16.09960
## sample estimates:
## mean of x mean of y
## 47.19283 68.97396driving a truck
t.test(algfirst$alg_11, aifirst$ai_11)##
## Welch Two Sample t-test
##
## data: algfirst$alg_11 and aifirst$ai_11
## t = -0.63105, df = 419.23, p-value = 0.5284
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -8.268846 4.249843
## sample estimates:
## mean of x mean of y
## 41.85149 43.86099t.test(humfirst$humanqual_11, humavgfirst$humanavg_11)##
## Welch Two Sample t-test
##
## data: humfirst$humanqual_11 and humavgfirst$humanavg_11
## t = 9.035, df = 337.44, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 18.21388 28.35169
## sample estimates:
## mean of x mean of y
## 79.64737 56.36458t.test(aifirst$ai_11, humavgfirst$humanavg_11)##
## Welch Two Sample t-test
##
## data: aifirst$ai_11 and humavgfirst$humanavg_11
## t = -4.0872, df = 412.32, p-value = 5.25e-05
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -18.517240 -6.489954
## sample estimates:
## mean of x mean of y
## 43.86099 56.36458airplane
t.test(algfirst$alg_12, aifirst$ai_12)##
## Welch Two Sample t-test
##
## data: algfirst$alg_12 and aifirst$ai_12
## t = -0.3518, df = 418.85, p-value = 0.7252
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -7.389084 5.145688
## sample estimates:
## mean of x mean of y
## 43.32673 44.44843t.test(humfirst$humanqual_12, humavgfirst$humanavg_12)##
## Welch Two Sample t-test
##
## data: humfirst$humanqual_12 and humavgfirst$humanavg_12
## t = 17.985, df = 362.34, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 45.35683 56.49361
## sample estimates:
## mean of x mean of y
## 76.82105 25.89583t.test(aifirst$ai_12, humavgfirst$humanavg_12)##
## Welch Two Sample t-test
##
## data: aifirst$ai_12 and humavgfirst$humanavg_12
## t = 5.943, df = 410.1, p-value = 5.984e-09
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 12.41599 24.68920
## sample estimates:
## mean of x mean of y
## 44.44843 25.89583t.test(algfirst$alg_12, humfirst$humanqual_12)##
## Welch Two Sample t-test
##
## data: algfirst$alg_12 and humfirst$humanqual_12
## t = -11.53, df = 369.75, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -39.20667 -27.78197
## sample estimates:
## mean of x mean of y
## 43.32673 76.82105driving a subway
t.test(algfirst$alg_13, aifirst$ai_13)##
## Welch Two Sample t-test
##
## data: algfirst$alg_13 and aifirst$ai_13
## t = -0.47475, df = 418.71, p-value = 0.6352
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -7.851851 4.796885
## sample estimates:
## mean of x mean of y
## 50.58911 52.11659t.test(humfirst$humanqual_13, humavgfirst$humanavg_13)##
## Welch Two Sample t-test
##
## data: humfirst$humanqual_13 and humavgfirst$humanavg_13
## t = 14.681, df = 368.85, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 37.12751 48.61141
## sample estimates:
## mean of x mean of y
## 72.96842 30.09896t.test(aifirst$ai_13, humavgfirst$humanavg_13)##
## Welch Two Sample t-test
##
## data: aifirst$ai_13 and humavgfirst$humanavg_13
## t = 6.9847, df = 409.89, p-value = 1.159e-11
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 15.82103 28.21424
## sample estimates:
## mean of x mean of y
## 52.11659 30.09896t.test(algfirst$alg_13, humfirst$humanqual_13)##
## Welch Two Sample t-test
##
## data: algfirst$alg_13 and humfirst$humanqual_13
## t = -7.484, df = 376.89, p-value = 5.137e-13
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -28.25901 -16.49961
## sample estimates:
## mean of x mean of y
## 50.58911 72.96842predicting weather
t.test(algfirst$alg_14, aifirst$ai_14)##
## Welch Two Sample t-test
##
## data: algfirst$alg_14 and aifirst$ai_14
## t = 0.92812, df = 417.87, p-value = 0.3539
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2.385867 6.654393
## sample estimates:
## mean of x mean of y
## 69.42574 67.29148t.test(humfirst$humanqual_14, humavgfirst$humanavg_14)##
## Welch Two Sample t-test
##
## data: humfirst$humanqual_14 and humavgfirst$humanavg_14
## t = 6.4006, df = 378.48, p-value = 4.581e-10
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 11.15491 21.04728
## sample estimates:
## mean of x mean of y
## 56.20526 40.10417t.test(aifirst$ai_14, humavgfirst$humanavg_14)##
## Welch Two Sample t-test
##
## data: aifirst$ai_14 and humavgfirst$humanavg_14
## t = 11.227, df = 392.44, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 22.42656 31.94807
## sample estimates:
## mean of x mean of y
## 67.29148 40.10417t.test(algfirst$alg_14, humfirst$humanqual_14)##
## Welch Two Sample t-test
##
## data: algfirst$alg_14 and humfirst$humanqual_14
## t = 5.5123, df = 388.8, p-value = 6.464e-08
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 8.505084 17.935874
## sample estimates:
## mean of x mean of y
## 69.42574 56.20526predicting stock market
t.test(algfirst$alg_15, aifirst$ai_15)##
## Welch Two Sample t-test
##
## data: algfirst$alg_15 and aifirst$ai_15
## t = 2.2749, df = 422.39, p-value = 0.02341
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.7446197 10.2089388
## sample estimates:
## mean of x mean of y
## 62.77723 57.30045t.test(humfirst$humanqual_15, humavgfirst$humanavg_15)##
## Welch Two Sample t-test
##
## data: humfirst$humanqual_15 and humavgfirst$humanavg_15
## t = 8.4937, df = 378.73, p-value = 4.624e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 16.43321 26.33345
## sample estimates:
## mean of x mean of y
## 52.21579 30.83246t.test(algfirst$alg_15, humavgfirst$humanavg_15)##
## Welch Two Sample t-test
##
## data: algfirst$alg_15 and humavgfirst$humanavg_15
## t = 12.906, df = 387.64, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 27.07844 36.81110
## sample estimates:
## mean of x mean of y
## 62.77723 30.83246t.test(algfirst$alg_15, humfirst$humanqual_15)##
## Welch Two Sample t-test
##
## data: algfirst$alg_15 and humfirst$humanqual_15
## t = 4.3334, df = 388.28, p-value = 1.873e-05
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 5.769618 15.353258
## sample estimates:
## mean of x mean of y
## 62.77723 52.21579predicting election
t.test(algfirst$alg_16, aifirst$ai_16)##
## Welch Two Sample t-test
##
## data: algfirst$alg_16 and aifirst$ai_16
## t = 0.28342, df = 420.71, p-value = 0.777
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -4.454268 5.955223
## sample estimates:
## mean of x mean of y
## 52.44554 51.69507t.test(humfirst$humanqual_16, humavgfirst$humanavg_16)##
## Welch Two Sample t-test
##
## data: humfirst$humanqual_16 and humavgfirst$humanavg_16
## t = 4.932, df = 379.95, p-value = 1.219e-06
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 7.195472 16.736436
## sample estimates:
## mean of x mean of y
## 48.73158 36.76562t.test(algfirst$alg_16, humavgfirst$humanavg_16)##
## Welch Two Sample t-test
##
## data: algfirst$alg_16 and humavgfirst$humanavg_16
## t = 6.1439, df = 389.7, p-value = 1.99e-09
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 10.66231 20.69752
## sample estimates:
## mean of x mean of y
## 52.44554 36.76562t.test(algfirst$alg_16, humfirst$humanqual_16)##
## Welch Two Sample t-test
##
## data: algfirst$alg_16 and humfirst$humanqual_16
## t = 1.4508, df = 388.36, p-value = 0.1476
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.318971 8.746902
## sample estimates:
## mean of x mean of y
## 52.44554 48.73158predicting employee performance
t.test(algfirst$alg_17, aifirst$ai_17)##
## Welch Two Sample t-test
##
## data: algfirst$alg_17 and aifirst$ai_17
## t = 0.48905, df = 421.03, p-value = 0.6251
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -3.799471 6.316320
## sample estimates:
## mean of x mean of y
## 48.41089 47.15247t.test(humfirst$humanqual_17, humavgfirst$humanavg_17)##
## Welch Two Sample t-test
##
## data: humfirst$humanqual_17 and humavgfirst$humanavg_17
## t = 7.3851, df = 374.6, p-value = 9.969e-13
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 13.46276 23.23318
## sample estimates:
## mean of x mean of y
## 60.40526 42.05729t.test(algfirst$alg_17, humavgfirst$humanavg_17)##
## Welch Two Sample t-test
##
## data: algfirst$alg_17 and humavgfirst$humanavg_17
## t = 2.4283, df = 391.37, p-value = 0.01562
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 1.209532 11.497667
## sample estimates:
## mean of x mean of y
## 48.41089 42.05729t.test(algfirst$alg_17, humfirst$humanqual_17)##
## Welch Two Sample t-test
##
## data: algfirst$alg_17 and humfirst$humanqual_17
## t = -4.8676, df = 387.51, p-value = 1.647e-06
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -16.839120 -7.149625
## sample estimates:
## mean of x mean of y
## 48.41089 60.40526predicting student performance
t.test(algfirst$alg_18, aifirst$ai_18)##
## Welch Two Sample t-test
##
## data: algfirst$alg_18 and aifirst$ai_18
## t = -0.70031, df = 421.66, p-value = 0.4841
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -6.669221 3.165336
## sample estimates:
## mean of x mean of y
## 43.39604 45.14798t.test(humfirst$humanqual_18, humavgfirst$humanavg_18)##
## Welch Two Sample t-test
##
## data: humfirst$humanqual_18 and humavgfirst$humanavg_18
## t = 9.3698, df = 358.5, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 17.74097 27.16649
## sample estimates:
## mean of x mean of y
## 63.05789 40.60417t.test(algfirst$alg_18, humavgfirst$humanavg_18)##
## Welch Two Sample t-test
##
## data: algfirst$alg_18 and humavgfirst$humanavg_18
## t = 1.0754, df = 388.8, p-value = 0.2829
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2.312251 7.895997
## sample estimates:
## mean of x mean of y
## 43.39604 40.60417t.test(algfirst$alg_18, humfirst$humanqual_18)##
## Welch Two Sample t-test
##
## data: algfirst$alg_18 and humfirst$humanqual_18
## t = -8.5382, df = 380.58, p-value = 3.304e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -24.18969 -15.13402
## sample estimates:
## mean of x mean of y
## 43.39604 63.05789predicting recidivism
t.test(algfirst$alg_19, aifirst$ai_19)##
## Welch Two Sample t-test
##
## data: algfirst$alg_19 and aifirst$ai_19
## t = 1.7037, df = 408.72, p-value = 0.0892
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.6859462 9.6037635
## sample estimates:
## mean of x mean of y
## 40.92079 36.46188t.test(humfirst$humanqual_19, humavgfirst$humanavg_19)##
## Welch Two Sample t-test
##
## data: humfirst$humanqual_19 and humavgfirst$humanavg_19
## t = 7.1545, df = 374.25, p-value = 4.457e-12
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 12.85485 22.59888
## sample estimates:
## mean of x mean of y
## 56.27895 38.55208t.test(algfirst$alg_19, humavgfirst$humanavg_19)##
## Welch Two Sample t-test
##
## data: algfirst$alg_19 and humavgfirst$humanavg_19
## t = 0.8731, df = 391.58, p-value = 0.3831
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2.965129 7.702546
## sample estimates:
## mean of x mean of y
## 40.92079 38.55208t.test(algfirst$alg_19, humfirst$humanqual_19)##
## Welch Two Sample t-test
##
## data: algfirst$alg_19 and humfirst$humanqual_19
## t = -5.9931, df = 380.82, p-value = 4.776e-09
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -20.39683 -10.31948
## sample estimates:
## mean of x mean of y
## 40.92079 56.27895buy sell stocks
t.test(algfirst$alg_20, aifirst$ai_20)##
## Welch Two Sample t-test
##
## data: algfirst$alg_20 and aifirst$ai_20
## t = 2.9212, df = 422.8, p-value = 0.003674
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 2.502144 12.795552
## sample estimates:
## mean of x mean of y
## 63.36634 55.71749t.test(humfirst$humanqual_20, humavgfirst$humanavg_20)##
## Welch Two Sample t-test
##
## data: humfirst$humanqual_20 and humavgfirst$humanavg_20
## t = 10.483, df = 373.06, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 21.40023 31.28234
## sample estimates:
## mean of x mean of y
## 59.26316 32.92188t.test(algfirst$alg_20, humavgfirst$humanavg_20)##
## Welch Two Sample t-test
##
## data: algfirst$alg_20 and humavgfirst$humanavg_20
## t = 11.56, df = 390.36, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 25.26653 35.62239
## sample estimates:
## mean of x mean of y
## 63.36634 32.92188t.test(algfirst$alg_20, humfirst$humanqual_20)##
## Welch Two Sample t-test
##
## data: algfirst$alg_20 and humfirst$humanqual_20
## t = 1.6693, df = 387.98, p-value = 0.09586
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.7294357 8.9357932
## sample estimates:
## mean of x mean of y
## 63.36634 59.26316diagnose disease
t.test(algfirst$alg_21, aifirst$ai_21)##
## Welch Two Sample t-test
##
## data: algfirst$alg_21 and aifirst$ai_21
## t = 0.51386, df = 421.07, p-value = 0.6076
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -3.996753 6.826083
## sample estimates:
## mean of x mean of y
## 49.21287 47.79821t.test(humfirst$humanqual_21, humavgfirst$humanavg_21)##
## Welch Two Sample t-test
##
## data: humfirst$humanqual_21 and humavgfirst$humanavg_21
## t = 18.655, df = 371.61, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 43.91889 54.26872
## sample estimates:
## mean of x mean of y
## 72.00526 22.91146t.test(aifirst$ai_21, humavgfirst$humanavg_21)##
## Welch Two Sample t-test
##
## data: aifirst$ai_21 and humavgfirst$humanavg_21
## t = 8.9552, df = 407.91, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 19.42375 30.34974
## sample estimates:
## mean of x mean of y
## 47.79821 22.91146t.test(algfirst$alg_21, humfirst$humanqual_21)##
## Welch Two Sample t-test
##
## data: algfirst$alg_21 and humfirst$humanqual_21
## t = -8.752, df = 385.59, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -27.91272 -17.67206
## sample estimates:
## mean of x mean of y
## 49.21287 72.00526hire fire employees
t.test(algfirst$alg_22, aifirst$ai_22)##
## Welch Two Sample t-test
##
## data: algfirst$alg_22 and aifirst$ai_22
## t = 0.80773, df = 419, p-value = 0.4197
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2.817847 6.749206
## sample estimates:
## mean of x mean of y
## 31.68317 29.71749t.test(humfirst$humanqual_22, humavgfirst$humanavg_22)##
## Welch Two Sample t-test
##
## data: humfirst$humanqual_22 and humavgfirst$humanavg_22
## t = 12.006, df = 361.5, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 25.49692 35.48520
## sample estimates:
## mean of x mean of y
## 69.64211 39.15104t.test(aifirst$ai_22, humavgfirst$humanavg_22)##
## Welch Two Sample t-test
##
## data: aifirst$ai_22 and humavgfirst$humanavg_22
## t = -3.6202, df = 389.55, p-value = 0.0003333
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -14.556793 -4.310313
## sample estimates:
## mean of x mean of y
## 29.71749 39.15104t.test(algfirst$alg_22, humfirst$humanqual_22)##
## Welch Two Sample t-test
##
## data: algfirst$alg_22 and humfirst$humanqual_22
## t = -16.068, df = 387.45, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -42.60379 -33.31408
## sample estimates:
## mean of x mean of y
## 31.68317 69.64211giving directions
t.test(algfirst$alg_23, aifirst$ai_23)##
## Welch Two Sample t-test
##
## data: algfirst$alg_23 and aifirst$ai_23
## t = 0.031045, df = 417.1, p-value = 0.9752
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -3.69510 3.81369
## sample estimates:
## mean of x mean of y
## 83.15347 83.09417t.test(humfirst$humanqual_23, humavgfirst$humanavg_23)##
## Welch Two Sample t-test
##
## data: humfirst$humanqual_23 and humavgfirst$humanavg_23
## t = 2.8613, df = 378.47, p-value = 0.004454
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 2.238569 12.074479
## sample estimates:
## mean of x mean of y
## 71.02632 63.86979t.test(aifirst$ai_23, humavgfirst$humanavg_23)##
## Welch Two Sample t-test
##
## data: aifirst$ai_23 and humavgfirst$humanavg_23
## t = 8.9212, df = 369.12, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 14.98695 23.46181
## sample estimates:
## mean of x mean of y
## 83.09417 63.86979t.test(algfirst$alg_23, humfirst$humanqual_23)##
## Welch Two Sample t-test
##
## data: algfirst$alg_23 and humfirst$humanqual_23
## t = 5.2875, df = 359.85, p-value = 2.156e-07
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 7.616719 16.637581
## sample estimates:
## mean of x mean of y
## 83.15347 71.02632play piano
t.test(algfirst$alg_24, aifirst$ai_24)##
## Welch Two Sample t-test
##
## data: algfirst$alg_24 and aifirst$ai_24
## t = -2.0878, df = 390.24, p-value = 0.03747
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -11.8092417 -0.3545909
## sample estimates:
## mean of x mean of y
## 67.41584 73.49776t.test(humfirst$humanqual_24, humavgfirst$humanavg_24)##
## Welch Two Sample t-test
##
## data: humfirst$humanqual_24 and humavgfirst$humanavg_24
## t = 13.021, df = 340.28, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 29.98806 40.66008
## sample estimates:
## mean of x mean of y
## 81.61053 46.28646t.test(aifirst$ai_24, humavgfirst$humanavg_24)##
## Welch Two Sample t-test
##
## data: aifirst$ai_24 and humavgfirst$humanavg_24
## t = 9.513, df = 381.5, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 21.58708 32.83552
## sample estimates:
## mean of x mean of y
## 73.49776 46.28646t.test(aifirst$ai_24, humfirst$humanqual_24)##
## Welch Two Sample t-test
##
## data: aifirst$ai_24 and humfirst$humanqual_24
## t = -3.4194, df = 409.21, p-value = 0.0006905
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -12.776662 -3.448875
## sample estimates:
## mean of x mean of y
## 73.49776 81.61053compose song
t.test(algfirst$alg_25, aifirst$ai_25)##
## Welch Two Sample t-test
##
## data: algfirst$alg_25 and aifirst$ai_25
## t = 0.051985, df = 415.95, p-value = 0.9586
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -5.561230 5.863365
## sample estimates:
## mean of x mean of y
## 43.98515 43.83408t.test(humfirst$humanqual_25, humavgfirst$humanavg_25)##
## Welch Two Sample t-test
##
## data: humfirst$humanqual_25 and humavgfirst$humanavg_25
## t = 13.174, df = 357.31, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 31.04479 41.94018
## sample estimates:
## mean of x mean of y
## 78.77895 42.28646t.test(aifirst$ai_25, humavgfirst$humanavg_25)##
## Welch Two Sample t-test
##
## data: aifirst$ai_25 and humavgfirst$humanavg_25
## t = 0.52449, df = 399.53, p-value = 0.6002
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -4.253219 7.348464
## sample estimates:
## mean of x mean of y
## 43.83408 42.28646t.test(algfirst$alg_25, humfirst$humanqual_25)##
## Welch Two Sample t-test
##
## data: algfirst$alg_25 and humfirst$humanqual_25
## t = -12.78, df = 374.95, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -40.14692 -29.44068
## sample estimates:
## mean of x mean of y
## 43.98515 78.77895schedule events
t.test(algfirst$alg_26, aifirst$ai_26)##
## Welch Two Sample t-test
##
## data: algfirst$alg_26 and aifirst$ai_26
## t = -0.82571, df = 420.13, p-value = 0.4094
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -6.714024 2.741862
## sample estimates:
## mean of x mean of y
## 69.00495 70.99103t.test(humfirst$humanqual_26, humavgfirst$humanavg_26)##
## Welch Two Sample t-test
##
## data: humfirst$humanqual_26 and humavgfirst$humanavg_26
## t = 9.4127, df = 355.57, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 17.31261 26.45789
## sample estimates:
## mean of x mean of y
## 75.98421 54.09896t.test(aifirst$ai_26, humavgfirst$humanavg_26)##
## Welch Two Sample t-test
##
## data: aifirst$ai_26 and humavgfirst$humanavg_26
## t = 6.7712, df = 400.53, p-value = 4.564e-11
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 11.98774 21.79641
## sample estimates:
## mean of x mean of y
## 70.99103 54.09896t.test(algfirst$alg_26, humfirst$humanqual_26)##
## Welch Two Sample t-test
##
## data: algfirst$alg_26 and humfirst$humanqual_26
## t = -3.1313, df = 378.78, p-value = 0.001876
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -11.361835 -2.596685
## sample estimates:
## mean of x mean of y
## 69.00495 75.98421analyze data
t.test(algfirst$alg_27, aifirst$ai_27)##
## Welch Two Sample t-test
##
## data: algfirst$alg_27 and aifirst$ai_27
## t = 1.6922, df = 417.22, p-value = 0.09136
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.5147908 6.8847238
## sample estimates:
## mean of x mean of y
## 82.05941 78.87444t.test(humfirst$humanqual_27, humavgfirst$humanavg_27)##
## Welch Two Sample t-test
##
## data: humfirst$humanqual_27 and humavgfirst$humanavg_27
## t = 9.1001, df = 376.09, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 18.57505 28.81475
## sample estimates:
## mean of x mean of y
## 65.71053 42.01562t.test(aifirst$ai_27, humavgfirst$humanavg_27)##
## Welch Two Sample t-test
##
## data: aifirst$ai_27 and humavgfirst$humanavg_27
## t = 15.277, df = 364.28, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 32.11429 41.60334
## sample estimates:
## mean of x mean of y
## 78.87444 42.01562t.test(algfirst$alg_27, humfirst$humanqual_27)##
## Welch Two Sample t-test
##
## data: algfirst$alg_27 and humfirst$humanqual_27
## t = 7.7057, df = 341.79, p-value = 1.426e-13
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 12.17572 20.52204
## sample estimates:
## mean of x mean of y
## 82.05941 65.71053setwd("~/Documents/Dropbox/Research/Adrian")
a<-read.csv ("algorithm task ratings.csv", header=T, sep=",")
names(a)## [1] "alg_1" "alg_2" "alg_3" "alg_4"
## [5] "alg_5" "alg_6" "alg_7" "alg_8"
## [9] "alg_9" "alg_10" "alg_11" "alg_12"
## [13] "alg_13" "alg_14" "alg_15" "alg_16"
## [17] "alg_17" "alg_18" "alg_19" "alg_20"
## [21] "alg_21" "alg_22" "alg_23" "alg_24"
## [25] "alg_25" "alg_26" "alg_27" "obj_1"
## [29] "obj_2" "obj_3" "obj_4" "obj_5"
## [33] "obj_6" "obj_7" "obj_8" "obj_9"
## [37] "obj_10" "obj_11" "obj_12" "obj_13"
## [41] "obj_14" "obj_15" "obj_16" "obj_17"
## [45] "obj_18" "obj_19" "obj_20" "obj_21"
## [49] "obj_22" "obj_23" "obj_24" "obj_25"
## [53] "obj_26" "obj_27" "cons_1" "cons_2"
## [57] "cons_3" "cons_4" "cons_5" "cons_6"
## [61] "cons_7" "cons_8" "cons_9" "cons_10"
## [65] "cons_11" "cons_12" "cons_13" "cons_14"
## [69] "cons_15" "cons_16" "cons_17" "cons_18"
## [73] "cons_19" "cons_20" "cons_21" "cons_22"
## [77] "cons_23" "cons_24" "cons_25" "cons_26"
## [81] "cons_27" "exp_1" "exp_2" "exp_3"
## [85] "exp_4" "exp_5" "exp_6" "exp_7"
## [89] "exp_8" "exp_9" "exp_10" "exp_11"
## [93] "exp_12" "exp_13" "exp_14" "exp_15"
## [97] "exp_16" "exp_17" "exp_18" "exp_19"
## [101] "exp_20" "exp_21" "exp_22" "exp_23"
## [105] "exp_24" "exp_25" "exp_26" "exp_27"
## [109] "alreadyalg_1" "alreadyalg_2" "alreadyalg_3" "alreadyalg_4"
## [113] "alreadyalg_5" "alreadyalg_6" "alreadyalg_7" "alreadyalg_8"
## [117] "alreadyalg_9" "alreadyalg_10" "alreadyalg_11" "alreadyalg_12"
## [121] "alreadyalg_13" "alreadyalg_14" "alreadyalg_15" "alreadyalg_16"
## [125] "alreadyalg_17" "alreadyalg_18" "alreadyalg_19" "alreadyalg_20"
## [129] "alreadyalg_21" "alreadyalg_22" "alreadyalg_23" "alreadyalg_24"
## [133] "alreadyalg_25" "alreadyalg_26" "alreadyalg_27"trust<-a[c(1:27)]
obj<-a[c(28:54)]
cons<-a[c(55:81)]
exp<-a[c(82:108)]
alr<-a[c(109:135)]
trustlong <- reshape(trust,
varying = c("alg_1", "alg_2", "alg_3", "alg_4", "alg_5", "alg_6", "alg_7", "alg_8", "alg_9", "alg_10", "alg_11", "alg_12", "alg_13", "alg_14", "alg_15", "alg_16", "alg_17", "alg_18", "alg_19", "alg_20", "alg_21", "alg_22", "alg_23", "alg_24", "alg_25", "alg_26", "alg_27"),
v.names = "trust",
timevar = "task",
times = c(1:27),
direction = "long")
objlong <- reshape(obj,
varying = c("obj_1", "obj_2", "obj_3", "obj_4", "obj_5", "obj_6", "obj_7", "obj_8", "obj_9", "obj_10", "obj_11", "obj_12", "obj_13", "obj_14", "obj_15", "obj_16", "obj_17", "obj_18", "obj_19", "obj_20", "obj_21", "obj_22", "obj_23", "obj_24", "obj_25", "obj_26", "obj_27"),
v.names = "obj",
timevar = "task",
times = c(1:27),
direction = "long")
conslong <- reshape(cons,
varying = c("cons_1", "cons_2", "cons_3", "cons_4", "cons_5", "cons_6", "cons_7", "cons_8", "cons_9", "cons_10", "cons_11", "cons_12", "cons_13", "cons_14", "cons_15", "cons_16", "cons_17", "cons_18", "cons_19", "cons_20", "cons_21", "cons_22", "cons_23", "cons_24", "cons_25", "cons_26", "cons_27"),
v.names = "cons",
timevar = "task",
times = c(1:27),
direction = "long")
explong <- reshape(exp,
varying = c("exp_1", "exp_2", "exp_3", "exp_4", "exp_5", "exp_6", "exp_7", "exp_8", "exp_9", "exp_10", "exp_11", "exp_12", "exp_13", "exp_14", "exp_15", "exp_16", "exp_17", "exp_18", "exp_19", "exp_20", "exp_21", "exp_22", "exp_23", "exp_24", "exp_25", "exp_26", "exp_27"),
v.names = "exp",
timevar = "task",
times = c(1:27),
direction = "long")
alrlong <- reshape(alr,
varying = c("alreadyalg_1", "alreadyalg_2", "alreadyalg_3", "alreadyalg_4", "alreadyalg_5", "alreadyalg_6", "alreadyalg_7", "alreadyalg_8", "alreadyalg_9", "alreadyalg_10", "alreadyalg_11", "alreadyalg_12", "alreadyalg_13", "alreadyalg_14", "alreadyalg_15", "alreadyalg_16", "alreadyalg_17", "alreadyalg_18", "alreadyalg_19", "alreadyalg_20", "alreadyalg_21", "alreadyalg_22", "alreadyalg_23", "alreadyalg_24", "alreadyalg_25", "alreadyalg_26", "alreadyalg_27"),
v.names = "alreadyalg",
timevar = "task",
times = c(1:27),
direction = "long")Objective/subjective
cor.test(trustlong$trust, objlong$obj)##
## Pearson's product-moment correlation
##
## data: trustlong$trust and objlong$obj
## t = 11.515, df = 2930, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1731839 0.2424515
## sample estimates:
## cor
## 0.2080786Consequential/inconsequential
cor.test(trustlong$trust, conslong$cons)##
## Pearson's product-moment correlation
##
## data: trustlong$trust and conslong$cons
## t = 6.3856, df = 2714, p-value = 2.002e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.08443728 0.15854800
## sample estimates:
## cor
## 0.1216622Personal experience
cor.test(trustlong$trust, explong$exp)##
## Pearson's product-moment correlation
##
## data: trustlong$trust and explong$exp
## t = 3.0767, df = 2725, p-value = 0.002114
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.02134922 0.09616040
## sample estimates:
## cor
## 0.05883742Belief that people already use algorithms for this
cor.test(trustlong$trust, alrlong$alreadyalg)##
## Pearson's product-moment correlation
##
## data: trustlong$trust and alrlong$alreadyalg
## t = 13.743, df = 2779, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2171330 0.2867465
## sample estimates:
## cor
## 0.2522661Now using gap between algorithms and humans instead of just trust in algorithms
a <- read.csv("AI_algo_2_humans.csv", header = T, sep = ",")
hum <- a[c(69:95)]
hum <- subset(hum, humanqual_1 != "NA")
hum <- head(hum, -25)
humlong <- reshape(hum, varying = c("humanqual_1", "humanqual_2", "humanqual_3",
"humanqual_4", "humanqual_5", "humanqual_6", "humanqual_7", "humanqual_8",
"humanqual_9", "humanqual_10", "humanqual_11", "humanqual_12", "humanqual_13",
"humanqual_14", "humanqual_15", "humanqual_16", "humanqual_17", "humanqual_18",
"humanqual_19", "humanqual_20", "humanqual_21", "humanqual_22", "humanqual_23",
"humanqual_24", "humanqual_25", "humanqual_26", "humanqual_27"), v.names = "humanqual",
timevar = "task", times = c(1:27), direction = "long")
names(humlong)## [1] "task" "humanqual" "id"names(trustlong)## [1] "task" "trust" "id"trustlong$gap <- humlong$humanqual - trustlong$trustObjective/subjective
cor.test(trustlong$gap, objlong$obj)##
## Pearson's product-moment correlation
##
## data: trustlong$gap and objlong$obj
## t = -9.4875, df = 2930, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2075451 -0.1373019
## sample estimates:
## cor
## -0.172643Consequential/inconsequential
cor.test(trustlong$gap, conslong$cons)##
## Pearson's product-moment correlation
##
## data: trustlong$gap and conslong$cons
## t = -4.9329, df = 2714, p-value = 8.589e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.13141288 -0.05685781
## sample estimates:
## cor
## -0.09426752Personal experience
cor.test(trustlong$gap, explong$exp)##
## Pearson's product-moment correlation
##
## data: trustlong$gap and explong$exp
## t = 0.08751, df = 2725, p-value = 0.9303
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.03586122 0.03920927
## sample estimates:
## cor
## 0.001676385Belief that people already use algorithms for this
cor.test(trustlong$gap, alrlong$alreadyalg)##
## Pearson's product-moment correlation
##
## data: trustlong$gap and alrlong$alreadyalg
## t = -15.969, df = 2779, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3235893 -0.2554911
## sample estimates:
## cor
## -0.2899071