\(H_0\): \(p_{bio}\) = \(p_{calc}\)
\(H_a\): \(p_{bio}\) > \(p_{calc}\)
Significance Level: \(\alpha = 0.05\)
p-value (< 2.2e-16):
# Data
bio_total <- 144790
bio_female <- 84200
calc_total <- 211693
calc_female <- 102598
# Two-proportion z-test
prop.test(
x = c(bio_female, calc_female),
n = c(bio_total, calc_total),
alternative = "greater",
correct = FALSE
)
##
## 2-sample test for equality of proportions without continuity correction
##
## data: c(bio_female, calc_female) out of c(bio_total, calc_total)
## X-squared = 3235.3, df = 1, p-value < 2.2e-16
## alternative hypothesis: greater
## 95 percent confidence interval:
## 0.09409523 1.00000000
## sample estimates:
## prop 1 prop 2
## 0.5815319 0.4846547
\(H_0\): \(\mu_{held}\) = \(\mu_{conv}\)
\(H_a\): \(\mu_{held}\) < \(\mu_{conv}\)
Significance Level: \(\alpha = 0.05\)
p-value (0.4881):
# Crying times (seconds)
conv <- c(
63, 0, 2, 46, 33, 33,
29, 23, 11, 12, 48, 15,
33, 14, 51, 37, 24, 70,
63, 0, 73, 39, 54, 52,
39, 34, 30, 55, 58, 18
)
held <- c(
0, 32, 20, 23, 14, 19,
60, 59, 64, 64, 72, 50,
44, 14, 10, 58, 19, 41,
17, 5, 36, 73, 19, 46,
9, 43, 73, 27, 25, 18
)
t.test(held, conv, alternative = "less", var.equal = FALSE)
##
## Welch Two Sample t-test
##
## data: held and conv
## t = -0.029953, df = 57.707, p-value = 0.4881
## alternative hypothesis: true difference in means is less than 0
## 95 percent confidence interval:
## -Inf 9.135003
## sample estimates:
## mean of x mean of y
## 35.13333 35.30000