Specifying and hypothesizing

In Chapter 3, you ran a two sample proportion test on the proportion of late shipments across freight cost groups. Recall the hypotheses.

\(H_{0}\): \(late_{\text{expensive}} - late_{\text{reasonable}} = 0\)

\(H_{A}\): \(late_{\text{expensive}} - late_{\text{reasonable}} > 0\)

Let's compare that traditional approach using prop_test() with a simulation-based infer pipeline.

late_shipments is available; dplyr and infer are loaded.

Run the proportion test code (previously seen in Chapter 3). Assuming a significance level of alpha = 0.05, what does the evidence suggest?

The p-value is less than or equal to the significance level, so you should reject the null hypothesis that the proportion of late shipments is the same for each freight cost group.
The p-value is less than or equal to the significance level, so you should fail to reject the null hypothesis that the proportion of late shipments is the same for each freight cost group.
The p-value is greater than the significance level, so you should reject the null hypothesis that the proportion of late shipments is the same for each freight cost group.
The p-value is greater than the significance level, so you should fail to reject the null hypothesis that the proportion of late shipments is the same for each freight cost group.