Check correlations between conservatism and comfort in each of the conditions, one task at a time. A c before “comfort” means control condition; u means upwards anthro, b means benefits condition, d means downwards, r means risk. First is driverless car:
cor.test(a$poli2_1, a$ccomfort_1)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$ccomfort_1
## t = 2.1737, df = 98, p-value = 0.03213
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.01884674 0.39427963
## sample estimates:
## cor
## 0.2144709
cor.test(a$poli2_1, a$ucomfort_1)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$ucomfort_1
## t = 2.6745, df = 101, p-value = 0.008731
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.06697989 0.42932848
## sample estimates:
## cor
## 0.257171
cor.test(a$poli2_1, a$bcomfort_1)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$bcomfort_1
## t = 2.7958, df = 97, p-value = 0.006242
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.07997994 0.44642548
## sample estimates:
## cor
## 0.2730801
cor.test(a$poli2_1, a$dcomfort_1)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$dcomfort_1
## t = 0.66669, df = 93, p-value = 0.5066
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1344438 0.2668024
## sample estimates:
## cor
## 0.06896797
cor.test(a$poli2_1, a$rcomfort_1)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$rcomfort_1
## t = 1.5098, df = 93, p-value = 0.1345
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.04837722 0.34544807
## sample estimates:
## cor
## 0.1546739
Medical diagnoses
cor.test(a$poli2_1, a$ccomfort_2)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$ccomfort_2
## t = 1.9606, df = 98, p-value = 0.05276
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.002222778 0.376337197
## sample estimates:
## cor
## 0.1942802
cor.test(a$poli2_1, a$ucomfort_2)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$ucomfort_2
## t = 2.0626, df = 101, p-value = 0.04172
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.007821741 0.379790399
## sample estimates:
## cor
## 0.2010421
cor.test(a$poli2_1, a$bcomfort_2)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$bcomfort_2
## t = 2.328, df = 97, p-value = 0.02199
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.03417437 0.40887834
## sample estimates:
## cor
## 0.2300343
cor.test(a$poli2_1, a$dcomfort_2)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$dcomfort_2
## t = 0.78713, df = 93, p-value = 0.4332
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1221959 0.2783302
## sample estimates:
## cor
## 0.0813506
cor.test(a$poli2_1, a$rcomfort_2)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$rcomfort_2
## t = 0.33295, df = 93, p-value = 0.7399
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1682076 0.2344180
## sample estimates:
## cor
## 0.03450522
So far the downward and risk conditions are the only ones that eliminate the correlation.
Next is surgery:
cor.test(a$poli2_1, a$ccomfort_3)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$ccomfort_3
## t = 2.0682, df = 98, p-value = 0.04125
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.008427225 0.385442180
## sample estimates:
## cor
## 0.2045068
cor.test(a$poli2_1, a$ucomfort_3)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$ucomfort_3
## t = 1.6451, df = 101, p-value = 0.1031
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.03301131 0.34430601
## sample estimates:
## cor
## 0.1615454
cor.test(a$poli2_1, a$bcomfort_3)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$bcomfort_3
## t = 1.3249, df = 97, p-value = 0.1883
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.06582412 0.32225065
## sample estimates:
## cor
## 0.1333201
cor.test(a$poli2_1, a$dcomfort_3)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$dcomfort_3
## t = -0.18668, df = 92, p-value = 0.8523
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2212035 0.1838833
## sample estimates:
## cor
## -0.01945862
cor.test(a$poli2_1, a$rcomfort_3)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$rcomfort_3
## t = 0.8875, df = 93, p-value = 0.3771
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1119688 0.2878683
## sample estimates:
## cor
## 0.09164221
Dating advice - very different pattern.
cor.test(a$poli2_1, a$ccomfort_4)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$ccomfort_4
## t = -0.5473, df = 98, p-value = 0.5854
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2489209 0.1427643
## sample estimates:
## cor
## -0.05520178
cor.test(a$poli2_1, a$ucomfort_4)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$ucomfort_4
## t = 0.1207, df = 101, p-value = 0.9042
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1819387 0.2050569
## sample estimates:
## cor
## 0.01200882
cor.test(a$poli2_1, a$bcomfort_4)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$bcomfort_4
## t = 0.074482, df = 97, p-value = 0.9408
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1901334 0.2046685
## sample estimates:
## cor
## 0.007562252
cor.test(a$poli2_1, a$dcomfort_4)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$dcomfort_4
## t = 2.0484, df = 93, p-value = 0.04334
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.006504097 0.392866259
## sample estimates:
## cor
## 0.2077747
cor.test(a$poli2_1, a$rcomfort_4)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$rcomfort_4
## t = -0.58806, df = 93, p-value = 0.5579
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2592294 0.1424237
## sample estimates:
## cor
## -0.06086643
Movie suggestions
cor.test(a$poli2_1, a$ccomfort_5)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$ccomfort_5
## t = 0.65032, df = 98, p-value = 0.517
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1325741 0.2586390
## sample estimates:
## cor
## 0.06555095
cor.test(a$poli2_1, a$ucomfort_5)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$ucomfort_5
## t = 0.34294, df = 101, p-value = 0.7324
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1604802 0.2261358
## sample estimates:
## cor
## 0.03410362
cor.test(a$poli2_1, a$bcomfort_5)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$bcomfort_5
## t = -0.31041, df = 97, p-value = 0.7569
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2274990 0.1669481
## sample estimates:
## cor
## -0.031502
cor.test(a$poli2_1, a$dcomfort_5)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$dcomfort_5
## t = 1.2639, df = 93, p-value = 0.2094
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.07352086 0.32303048
## sample estimates:
## cor
## 0.1299477
cor.test(a$poli2_1, a$rcomfort_5)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$rcomfort_5
## t = 3.2173, df = 93, p-value = 0.001781
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1227550 0.4869526
## sample estimates:
## cor
## 0.3164684
Personality analysis
cor.test(a$poli2_1, a$ccomfort_6)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$ccomfort_6
## t = -0.02354, df = 98, p-value = 0.9813
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1987032 0.1941309
## sample estimates:
## cor
## -0.002377921
cor.test(a$poli2_1, a$ucomfort_6)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$ucomfort_6
## t = 1.5061, df = 101, p-value = 0.1352
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.04665847 0.33220115
## sample estimates:
## cor
## 0.1482044
cor.test(a$poli2_1, a$bcomfort_6)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$bcomfort_6
## t = 0.97841, df = 97, p-value = 0.3303
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1005173 0.2905969
## sample estimates:
## cor
## 0.0988562
cor.test(a$poli2_1, a$dcomfort_6)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$dcomfort_6
## t = 1.077, df = 93, p-value = 0.2843
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.09262604 0.30569385
## sample estimates:
## cor
## 0.1109889
cor.test(a$poli2_1, a$rcomfort_6)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$rcomfort_6
## t = 0.33668, df = 92, p-value = 0.7371
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1687361 0.2360196
## sample estimates:
## cor
## 0.03508022
Job interview
cor.test(a$poli2_1, a$ccomfort_7)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$ccomfort_7
## t = -1.0993, df = 98, p-value = 0.2743
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.30027901 0.08795429
## sample estimates:
## cor
## -0.1103706
cor.test(a$poli2_1, a$ucomfort_7)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$ucomfort_7
## t = 0.62517, df = 101, p-value = 0.5333
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1330369 0.2525762
## sample estimates:
## cor
## 0.06208626
cor.test(a$poli2_1, a$bcomfort_7)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$bcomfort_7
## t = -0.44389, df = 97, p-value = 0.6581
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2403002 0.1537542
## sample estimates:
## cor
## -0.04502425
cor.test(a$poli2_1, a$dcomfort_7)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$dcomfort_7
## t = 0.20604, df = 93, p-value = 0.8372
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1809620 0.2219476
## sample estimates:
## cor
## 0.02136008
cor.test(a$poli2_1, a$rcomfort_7)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$rcomfort_7
## t = 0.184, df = 93, p-value = 0.8544
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1831712 0.2197741
## sample estimates:
## cor
## 0.019076
Job advice
cor.test(a$poli2_1, a$ccomfort_8)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$ccomfort_8
## t = 0.32534, df = 98, p-value = 0.7456
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1646340 0.2277947
## sample estimates:
## cor
## 0.03284625
cor.test(a$poli2_1, a$ucomfort_8)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$ucomfort_8
## t = -0.49642, df = 101, p-value = 0.6207
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2405636 0.1455788
## sample estimates:
## cor
## -0.04933577
cor.test(a$poli2_1, a$bcomfort_8)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$bcomfort_8
## t = -0.067616, df = 97, p-value = 0.9462
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2040005 0.1908052
## sample estimates:
## cor
## -0.006865193
cor.test(a$poli2_1, a$dcomfort_8)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$dcomfort_8
## t = -0.69812, df = 93, p-value = 0.4868
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2698194 0.1312501
## sample estimates:
## cor
## -0.07220284
cor.test(a$poli2_1, a$rcomfort_8)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$rcomfort_8
## t = 0.99588, df = 93, p-value = 0.3219
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1009103 0.2980934
## sample estimates:
## cor
## 0.1027218
closeness to humans
cor.test(a$poli2_1, a$cclose)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$cclose
## t = 0.057874, df = 98, p-value = 0.954
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1907911 0.2020322
## sample estimates:
## cor
## 0.005846068
cor.test(a$poli2_1, a$uclose)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$uclose
## t = 1.6901, df = 102, p-value = 0.09406
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.02844179 0.34662077
## sample estimates:
## cor
## 0.1650505
cor.test(a$poli2_1, a$bclose)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$bclose
## t = 0.15522, df = 97, p-value = 0.877
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1822204 0.2125091
## sample estimates:
## cor
## 0.01575831
cor.test(a$poli2_1, a$dclose)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$dclose
## t = 0.91077, df = 93, p-value = 0.3648
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1095954 0.2900706
## sample estimates:
## cor
## 0.09402447
cor.test(a$poli2_1, a$rclose)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$rclose
## t = 0.87484, df = 93, p-value = 0.3839
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1132596 0.2866689
## sample estimates:
## cor
## 0.09034568
risks
cor.test(a$poli2_1, a$crisk)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$crisk
## t = -1.6414, df = 98, p-value = 0.1039
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.34878846 0.03393161
## sample estimates:
## cor
## -0.1635767
cor.test(a$poli2_1, a$urisk)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$urisk
## t = -1.4649, df = 102, p-value = 0.146
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.32708719 0.05044158
## sample estimates:
## cor
## -0.1435411
cor.test(a$poli2_1, a$brisk)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$brisk
## t = -2.3259, df = 96, p-value = 0.02213
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.41057516 -0.03411344
## sample estimates:
## cor
## -0.2309708
cor.test(a$poli2_1, a$drisk)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$drisk
## t = -0.028228, df = 92, p-value = 0.9775
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2054374 0.1997932
## sample estimates:
## cor
## -0.002942946
cor.test(a$poli2_1, a$rrisk)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$rrisk
## t = -3.1171, df = 93, p-value = 0.00243
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4793917 -0.1130284
## sample estimates:
## cor
## -0.307565
benefits
cor.test(a$poli2_1, a$cbenefits)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$cbenefits
## t = 1.1489, df = 97, p-value = 0.2534
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.08345509 0.30627202
## sample estimates:
## cor
## 0.1158656
cor.test(a$poli2_1, a$ubenefits)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$ubenefits
## t = 2.2166, df = 102, p-value = 0.02887
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.02272326 0.39082600
## sample estimates:
## cor
## 0.2143733
cor.test(a$poli2_1, a$bbenefits)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$bbenefits
## t = 1.9574, df = 97, p-value = 0.05318
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.00258014 0.37780426
## sample estimates:
## cor
## 0.194931
cor.test(a$poli2_1, a$dbenefits)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$dbenefits
## t = 0.9406, df = 93, p-value = 0.3493
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1065527 0.2928877
## sample estimates:
## cor
## 0.09707495
cor.test(a$poli2_1, a$rbenefits)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$rbenefits
## t = 3.3042, df = 93, p-value = 0.001353
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1311563 0.4934397
## sample estimates:
## cor
## 0.3241313
job threat
cor.test(a$poli2_1, a$cjobthreat)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$cjobthreat
## t = 0.2469, df = 98, p-value = 0.8055
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1723293 0.2202722
## sample estimates:
## cor
## 0.02493279
cor.test(a$poli2_1, a$ujobs)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$ujobs
## t = 0.024245, df = 100, p-value = 0.9807
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1921410 0.1968066
## sample estimates:
## cor
## 0.00242448
cor.test(a$poli2_1, a$bjobs)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$bjobs
## t = 1.5944, df = 97, p-value = 0.1141
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.0388334 0.3462909
## sample estimates:
## cor
## 0.1598035
cor.test(a$poli2_1, a$djobs)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$djobs
## t = 0.43992, df = 92, p-value = 0.661
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1582697 0.2461485
## sample estimates:
## cor
## 0.04581654
cor.test(a$poli2_1, a$rjobs)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$rjobs
## t = -1.0657, df = 91, p-value = 0.2894
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.30777081 0.09482979
## sample estimates:
## cor
## -0.1110232
lives threat
cor.test(a$poli2_1, a$clivesthreat)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$clivesthreat
## t = -1.5633, df = 98, p-value = 0.1212
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.34192985 0.04170792
## sample estimates:
## cor
## -0.1559881
cor.test(a$poli2_1, a$ulives)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$ulives
## t = -2.1484, df = 100, p-value = 0.0341
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.38864235 -0.01623084
## sample estimates:
## cor
## -0.2100427
cor.test(a$poli2_1, a$blives)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$blives
## t = -2.143, df = 97, p-value = 0.03461
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.39350957 -0.01586858
## sample estimates:
## cor
## -0.2126144
cor.test(a$poli2_1, a$dlives)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$dlives
## t = -1.648, df = 93, p-value = 0.1027
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.35784320 0.03425717
## sample estimates:
## cor
## -0.1684489
cor.test(a$poli2_1, a$rlives)
##
## Pearson's product-moment correlation
##
## data: a$poli2_1 and a$rlives
## t = -2.1722, df = 93, p-value = 0.03238
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.40341925 -0.01904308
## sample estimates:
## cor
## -0.2197427
Do any of the conditions have main effects? Sometimes, and mostly negative.
For car:
t.test(a$ccomfort_1, a$dcomfort_1)
##
## Welch Two Sample t-test
##
## data: a$ccomfort_1 and a$dcomfort_1
## t = 1.8129, df = 199.46, p-value = 0.07135
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.02825918 0.67268618
## sample estimates:
## mean of x mean of y
## 2.903846 2.581633
t.test(a$ccomfort_1, a$ucomfort_1)
##
## Welch Two Sample t-test
##
## data: a$ccomfort_1 and a$ucomfort_1
## t = 1.1554, df = 206.73, p-value = 0.2493
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1540755 0.5903392
## sample estimates:
## mean of x mean of y
## 2.903846 2.685714
t.test(a$ccomfort_1, a$bcomfort_1)
##
## Welch Two Sample t-test
##
## data: a$ccomfort_1 and a$bcomfort_1
## t = 0.3354, df = 202.89, p-value = 0.7377
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.3037575 0.4282815
## sample estimates:
## mean of x mean of y
## 2.903846 2.841584
t.test(a$ccomfort_1, a$rcomfort_1)
##
## Welch Two Sample t-test
##
## data: a$ccomfort_1 and a$rcomfort_1
## t = 3.2467, df = 200.9, p-value = 0.001368
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.2253274 0.9223649
## sample estimates:
## mean of x mean of y
## 2.903846 2.330000
diagnosis
t.test(a$ccomfort_2, a$dcomfort_2)
##
## Welch Two Sample t-test
##
## data: a$ccomfort_2 and a$dcomfort_2
## t = 1.4575, df = 197.62, p-value = 0.1466
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.08944476 0.59611666
## sample estimates:
## mean of x mean of y
## 3.028846 2.775510
t.test(a$ccomfort_2, a$ucomfort_2)
##
## Welch Two Sample t-test
##
## data: a$ccomfort_2 and a$ucomfort_2
## t = 2.9351, df = 206.26, p-value = 0.003712
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.1564188 0.7965116
## sample estimates:
## mean of x mean of y
## 3.028846 2.552381
t.test(a$ccomfort_2, a$bcomfort_2)
##
## Welch Two Sample t-test
##
## data: a$ccomfort_2 and a$bcomfort_2
## t = 0.91768, df = 201.96, p-value = 0.3599
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1809816 0.4960996
## sample estimates:
## mean of x mean of y
## 3.028846 2.871287
t.test(a$ccomfort_2, a$rcomfort_2)
##
## Welch Two Sample t-test
##
## data: a$ccomfort_2 and a$rcomfort_2
## t = 0.58762, df = 201.72, p-value = 0.5574
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.2328404 0.4305327
## sample estimates:
## mean of x mean of y
## 3.028846 2.930000
surgery
t.test(a$ccomfort_3, a$dcomfort_3)
##
## Welch Two Sample t-test
##
## data: a$ccomfort_3 and a$dcomfort_3
## t = 1.2199, df = 198.66, p-value = 0.2239
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1374930 0.5835676
## sample estimates:
## mean of x mean of y
## 2.480769 2.257732
t.test(a$ccomfort_3, a$ucomfort_3)
##
## Welch Two Sample t-test
##
## data: a$ccomfort_3 and a$ucomfort_3
## t = 1.2708, df = 200.82, p-value = 0.2053
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1181039 0.5463090
## sample estimates:
## mean of x mean of y
## 2.480769 2.266667
t.test(a$ccomfort_3, a$bcomfort_3)
##
## Welch Two Sample t-test
##
## data: a$ccomfort_3 and a$bcomfort_3
## t = 0.41641, df = 202.96, p-value = 0.6776
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.2794900 0.4291472
## sample estimates:
## mean of x mean of y
## 2.480769 2.405941
t.test(a$ccomfort_3, a$rcomfort_3)
##
## Welch Two Sample t-test
##
## data: a$ccomfort_3 and a$rcomfort_3
## t = 1.8491, df = 200.57, p-value = 0.06591
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.0212910 0.6628295
## sample estimates:
## mean of x mean of y
## 2.480769 2.160000
dating advice
t.test(a$ccomfort_4, a$dcomfort_4)
##
## Welch Two Sample t-test
##
## data: a$ccomfort_4 and a$dcomfort_4
## t = 1.7758, df = 200, p-value = 0.07729
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.03291684 0.62907068
## sample estimates:
## mean of x mean of y
## 2.798077 2.500000
t.test(a$ccomfort_4, a$ucomfort_4)
##
## Welch Two Sample t-test
##
## data: a$ccomfort_4 and a$ucomfort_4
## t = 1.9048, df = 202.56, p-value = 0.05822
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.01063659 0.61631424
## sample estimates:
## mean of x mean of y
## 2.798077 2.495238
t.test(a$ccomfort_4, a$bcomfort_4)
##
## Welch Two Sample t-test
##
## data: a$ccomfort_4 and a$bcomfort_4
## t = 0.092694, df = 202.78, p-value = 0.9262
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.3222885 0.3540860
## sample estimates:
## mean of x mean of y
## 2.798077 2.782178
t.test(a$ccomfort_4, a$rcomfort_4)
##
## Welch Two Sample t-test
##
## data: a$ccomfort_4 and a$rcomfort_4
## t = -0.18124, df = 200.43, p-value = 0.8564
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.3792415 0.3153953
## sample estimates:
## mean of x mean of y
## 2.798077 2.830000
movie suggestions
t.test(a$ccomfort_5, a$dcomfort_5)
##
## Welch Two Sample t-test
##
## data: a$ccomfort_5 and a$dcomfort_5
## t = 1.4645, df = 175.5, p-value = 0.1448
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.06022865 0.40677496
## sample estimates:
## mean of x mean of y
## 4.336538 4.163265
t.test(a$ccomfort_5, a$ucomfort_5)
##
## Welch Two Sample t-test
##
## data: a$ccomfort_5 and a$ucomfort_5
## t = 2.0789, df = 199.71, p-value = 0.0389
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.01144167 0.43306382
## sample estimates:
## mean of x mean of y
## 4.336538 4.114286
t.test(a$ccomfort_5, a$bcomfort_5)
##
## Welch Two Sample t-test
##
## data: a$ccomfort_5 and a$bcomfort_5
## t = -2.0445, df = 202.02, p-value = 0.0422
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.369734895 -0.006693132
## sample estimates:
## mean of x mean of y
## 4.336538 4.524752
t.test(a$ccomfort_5, a$rcomfort_5)
##
## Welch Two Sample t-test
##
## data: a$ccomfort_5 and a$rcomfort_5
## t = 0.44541, df = 195.79, p-value = 0.6565
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1595226 0.2525996
## sample estimates:
## mean of x mean of y
## 4.336538 4.290000
personality analysis
t.test(a$ccomfort_6, a$dcomfort_6)
##
## Welch Two Sample t-test
##
## data: a$ccomfort_6 and a$dcomfort_6
## t = 0.55267, df = 199.9, p-value = 0.5811
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.2474230 0.4401232
## sample estimates:
## mean of x mean of y
## 3.259615 3.163265
t.test(a$ccomfort_6, a$ucomfort_6)
##
## Welch Two Sample t-test
##
## data: a$ccomfort_6 and a$ucomfort_6
## t = 2.1545, df = 203.37, p-value = 0.03237
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.03011201 0.67959495
## sample estimates:
## mean of x mean of y
## 3.259615 2.904762
t.test(a$ccomfort_6, a$bcomfort_6)
##
## Welch Two Sample t-test
##
## data: a$ccomfort_6 and a$bcomfort_6
## t = -1.3603, df = 194.92, p-value = 0.1753
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.52827330 0.09700902
## sample estimates:
## mean of x mean of y
## 3.259615 3.475248
t.test(a$ccomfort_6, a$rcomfort_6)
##
## Welch Two Sample t-test
##
## data: a$ccomfort_6 and a$rcomfort_6
## t = 0.38765, df = 200.91, p-value = 0.6987
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.2766542 0.4120466
## sample estimates:
## mean of x mean of y
## 3.259615 3.191919
job interview
t.test(a$ccomfort_7, a$dcomfort_7)
##
## Welch Two Sample t-test
##
## data: a$ccomfort_7 and a$dcomfort_7
## t = 1.9082, df = 199.79, p-value = 0.0578
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.01142876 0.69627963
## sample estimates:
## mean of x mean of y
## 2.740385 2.397959
t.test(a$ccomfort_7, a$ucomfort_7)
##
## Welch Two Sample t-test
##
## data: a$ccomfort_7 and a$ucomfort_7
## t = 2.7806, df = 202.01, p-value = 0.005938
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.1350304 0.7933579
## sample estimates:
## mean of x mean of y
## 2.740385 2.276190
t.test(a$ccomfort_7, a$bcomfort_7)
##
## Welch Two Sample t-test
##
## data: a$ccomfort_7 and a$bcomfort_7
## t = 1.3883, df = 202.96, p-value = 0.1666
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1031014 0.5937716
## sample estimates:
## mean of x mean of y
## 2.740385 2.495050
t.test(a$ccomfort_7, a$rcomfort_7)
##
## Welch Two Sample t-test
##
## data: a$ccomfort_7 and a$rcomfort_7
## t = 1.0234, df = 201.96, p-value = 0.3074
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1671748 0.5279440
## sample estimates:
## mean of x mean of y
## 2.740385 2.560000
job advice
t.test(a$ccomfort_8, a$dcomfort_8)
##
## Welch Two Sample t-test
##
## data: a$ccomfort_8 and a$dcomfort_8
## t = 1.3201, df = 198.68, p-value = 0.1883
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1025118 0.5177394
## sample estimates:
## mean of x mean of y
## 3.442308 3.234694
t.test(a$ccomfort_8, a$ucomfort_8)
##
## Welch Two Sample t-test
##
## data: a$ccomfort_8 and a$ucomfort_8
## t = 1.3858, df = 205.51, p-value = 0.1673
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.08632726 0.49475217
## sample estimates:
## mean of x mean of y
## 3.442308 3.238095
t.test(a$ccomfort_8, a$bcomfort_8)
##
## Welch Two Sample t-test
##
## data: a$ccomfort_8 and a$bcomfort_8
## t = -0.75067, df = 202.73, p-value = 0.4537
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.4067182 0.1824227
## sample estimates:
## mean of x mean of y
## 3.442308 3.554455
t.test(a$ccomfort_8, a$rcomfort_8)
##
## Welch Two Sample t-test
##
## data: a$ccomfort_8 and a$rcomfort_8
## t = 0.079273, df = 201.56, p-value = 0.9369
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.2938299 0.3184452
## sample estimates:
## mean of x mean of y
## 3.442308 3.430000
closeness to humans - they all backfired!
t.test(a$cclose, a$dclose)
##
## Welch Two Sample t-test
##
## data: a$cclose and a$dclose
## t = 3.0805, df = 198.69, p-value = 0.002359
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.1554227 0.7083920
## sample estimates:
## mean of x mean of y
## 2.778846 2.346939
t.test(a$cclose, a$uclose)
##
## Welch Two Sample t-test
##
## data: a$cclose and a$uclose
## t = 3.459, df = 205.12, p-value = 0.0006594
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.1929238 0.7043911
## sample estimates:
## mean of x mean of y
## 2.778846 2.330189
t.test(a$cclose, a$bclose)
##
## Welch Two Sample t-test
##
## data: a$cclose and a$bclose
## t = 1.4474, df = 202.95, p-value = 0.1493
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.07052942 0.45990489
## sample estimates:
## mean of x mean of y
## 2.778846 2.584158
t.test(a$cclose, a$rclose)
##
## Welch Two Sample t-test
##
## data: a$cclose and a$rclose
## t = 2.8386, df = 201.85, p-value = 0.004994
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.1156877 0.6420046
## sample estimates:
## mean of x mean of y
## 2.778846 2.400000
risk
t.test(a$crisk, a$drisk)
##
## Welch Two Sample t-test
##
## data: a$crisk and a$drisk
## t = -1.2017, df = 196.65, p-value = 0.2309
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.4372204 0.1061340
## sample estimates:
## mean of x mean of y
## 2.865385 3.030928
t.test(a$crisk, a$urisk)
##
## Welch Two Sample t-test
##
## data: a$crisk and a$urisk
## t = -1.518, df = 206.9, p-value = 0.1305
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.48293824 0.06276407
## sample estimates:
## mean of x mean of y
## 2.865385 3.075472
t.test(a$crisk, a$brisk)
##
## Welch Two Sample t-test
##
## data: a$crisk and a$brisk
## t = 0.11963, df = 201.63, p-value = 0.9049
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.2381929 0.2689621
## sample estimates:
## mean of x mean of y
## 2.865385 2.850000
t.test(a$crisk, a$rrisk)
##
## Welch Two Sample t-test
##
## data: a$crisk and a$rrisk
## t = -0.76346, df = 200.59, p-value = 0.4461
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.3748171 0.1655863
## sample estimates:
## mean of x mean of y
## 2.865385 2.970000
benefits
t.test(a$cbenefits, a$dbenefits)
##
## Welch Two Sample t-test
##
## data: a$cbenefits and a$dbenefits
## t = 1.1699, df = 197.84, p-value = 0.2434
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.09421046 0.36902718
## sample estimates:
## mean of x mean of y
## 4.106796 3.969388
t.test(a$cbenefits, a$ubenefits)
##
## Welch Two Sample t-test
##
## data: a$cbenefits and a$ubenefits
## t = 0.19326, df = 199.12, p-value = 0.847
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.2014748 0.2452558
## sample estimates:
## mean of x mean of y
## 4.106796 4.084906
t.test(a$cbenefits, a$bbenefits)
##
## Welch Two Sample t-test
##
## data: a$cbenefits and a$bbenefits
## t = -0.10787, df = 193.09, p-value = 0.9142
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.2317102 0.2076787
## sample estimates:
## mean of x mean of y
## 4.106796 4.118812
t.test(a$cbenefits, a$rbenefits)
##
## Welch Two Sample t-test
##
## data: a$cbenefits and a$rbenefits
## t = 0.96513, df = 191.22, p-value = 0.3357
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1114650 0.3250573
## sample estimates:
## mean of x mean of y
## 4.106796 4.000000
job threat
t.test(a$cjobthreat, a$djobs)
##
## Welch Two Sample t-test
##
## data: a$cjobthreat and a$djobs
## t = 1.2383, df = 192.5, p-value = 0.2171
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1108214 0.4847310
## sample estimates:
## mean of x mean of y
## 3.980769 3.793814
t.test(a$cjobthreat, a$ujobs)
##
## Welch Two Sample t-test
##
## data: a$cjobthreat and a$ujobs
## t = -0.36206, df = 203.93, p-value = 0.7177
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.3098863 0.2137324
## sample estimates:
## mean of x mean of y
## 3.980769 4.028846
t.test(a$cjobthreat, a$bjobs)
##
## Welch Two Sample t-test
##
## data: a$cjobthreat and a$bjobs
## t = 0.49384, df = 202.58, p-value = 0.622
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.2091203 0.3488765
## sample estimates:
## mean of x mean of y
## 3.980769 3.910891
t.test(a$cjobthreat, a$rjobs)
##
## Welch Two Sample t-test
##
## data: a$cjobthreat and a$rjobs
## t = 0.92044, df = 197.53, p-value = 0.3585
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1529004 0.4205613
## sample estimates:
## mean of x mean of y
## 3.980769 3.846939
lives threat
t.test(a$clives, a$dlives)
##
## Welch Two Sample t-test
##
## data: a$clives and a$dlives
## t = 0.61026, df = 198.62, p-value = 0.5424
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.2184953 0.4143352
## sample estimates:
## mean of x mean of y
## 3.067308 2.969388
t.test(a$clives, a$ulives)
##
## Welch Two Sample t-test
##
## data: a$clives and a$ulives
## t = 1.1656, df = 205.99, p-value = 0.2451
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1263151 0.4916997
## sample estimates:
## mean of x mean of y
## 3.067308 2.884615
t.test(a$clives, a$blives)
##
## Welch Two Sample t-test
##
## data: a$clives and a$blives
## t = 2.0625, df = 202.8, p-value = 0.04044
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.01428796 0.63517891
## sample estimates:
## mean of x mean of y
## 3.067308 2.742574
t.test(a$clives, a$rlives)
##
## Welch Two Sample t-test
##
## data: a$clives and a$rlives
## t = 1.4245, df = 201.21, p-value = 0.1558
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.08733303 0.54194841
## sample estimates:
## mean of x mean of y
## 3.067308 2.840000