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Simulation-based t-test

In Chapter 2 you manually performed the steps for a t-test to explore these hypotheses.

\(H_{0}\): The mean weight of shipments that weren't late is the same as the mean weight of shipments that were late.

\(H_{A}\): The mean weight of shipments that weren't late is less than the mean weight of shipments that were late.

You can run the test more concisely using infer's t_test().

late_shipments %>% 
  t_test(
    weight_kilograms ~ late,
    order = c("No", "Yes"),
    alternative = "less"
  )

t_test() assumes that the null distribution is normal. We can avoid assumptions by using a simulation-based non-parametric equivalent.

late_shipments is available; dplyr and infer are loaded.

This exercise is part of the course

Hypothesis Testing in R

View Course

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Fill out the null distribution pipeline
null_distn <- late_shipments %>% 
  # Specify weight_kilograms vs. late
  ___ %>% 
  # Declare a null hypothesis of independence
  ___ %>% 
  # Generate 1000 permutation replicates
  ___ %>% 
  # Calculate the difference in means ("No" minus "Yes")
  ___

# See the results
null_distn
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