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Comparing CLT- & simulation- based inference

What is similar, and what is different, between CLT based test of means vs. simulation based test?

Getting started

Clone the ae-14 repo on GitHub and start a new project in RStudio. Be sure to configure git in the RStudio console, so you can so you can push your results back up to GitHub.

library(usethis)
use_git_config(user.name= "github username", user.email="your email")

CLT-based inference with Pokemon

library(tidyverse)
library(infer)

We will be using the pokemon dataset, which contains information about 45 randomly selected Pokemon (from all generations). You may load in the dataset with the following code:

pokemon <- read_csv("data/pokemon.csv")

Use the infer package to answer the following exercises.

Exercise 1

Construct and interpret a 95% confidence interval for the mean height in meters (height_m) of Pokemon species by using the Central Limit Theorem.

Exercise 2

The average height of a human is 1.65 meters. Evaluate whether a randomly selected Pokemon species has a different mean height by using the Central Limit Theorem.

In doing so, state your null and alternative hypotheses, the distribution of your test statistic under the null hypothesis, your p-value, decision, and conclusion in context of the research problem.

Exercise 3

Now evaluate whether a randomly-selected Pokemon species has a lower mean height by using the Central Limit Theorem. In doing so, state your null and alternative hypotheses, the distribution of your test statistic under the null hypothesis, your p-value, decision, and conclusion in context of the research problem.

Exercise 4

Suppose the true mean height among Pokemon species is 1.4 meters. In your conclusions from Exercises 2 and 3, did you make the correct decision, a Type 1 error, or a Type 2 error? Explain.