# Hypothesis Tests II

STAT 20: Introduction to Probability and Statistics

## Agenda

• Announcements
• Concept Questions
• Break
• PS 14: Hypothesis Tests II

## Announcements

• RQ: Wrong By Design due Wed/Thu night at 11:59pm
• Quiz 3 is in the first half of class on Thursday/Friday.
• Problem Set 14 is due the Tuesday after break.

# Concept Questions

Which pair of plots would have the greatest chi-squared distance between them? (consider one of them the “observed” and the other the “expected”)

01:00

## Chi-squareds Compared

$\frac{(1-1)^2}{1} + \frac{(10 - 1)^2}{1} + \frac{(1 - 10)^2}{10} \\ 0 + 81 + \frac{81}{10} = 89.1$

$\frac{(3-5)^2}{5} + \frac{(4-4)^2}{4} + \frac{(5-3)^2}{3} \\ \frac{4}{5} + 0 + \frac{4}{3} = 2.13$

## An In-class Experiment

In order to demonstrate how to conduct a hypothesis test through simulation, we will be collecting data from this class using a poll.

You will have only 15 seconds to answer the following multiple choice question, so please get ready at pollev.com

The two shapes above have simple first names:

• Booba
• Kiki

Which of the two names belongs to the shape on the left?

00:15

## Steps of a Hypothesis Test

1. Assert a model for how the data was generated (the null hypothesis)
2. Select a test statistic that bears on that null hypothesis (a mean, a proportion, a difference in means, a difference in proportions, etc).
3. Approximate the sampling distribution of that statistic under the null hypothesis (aka the null distribution)
4. Assess the degree of consistency between that distribution and the test statistic that was actually observed (either visually or by calculating a p-value)

## 1. The Null Hypothesis

• Let $p_k$ be the probability that a person selects Kiki for the shape on the left.
• Let $\hat{p}_k$ be the sample proportion of people that selected Kiki for the shape on the left.

What is a statement of the null hypothesis that corresponds to the notion the link between names and shapes is arbitrary?

01:00

## 2. Select a test statistic

$\hat{p}_k = \frac{\textrm{Number who chose "Kiki"}}{\textrm{Total number of people}}$

Note: you could also simply $n_k$, the number of people who chose “Kiki”.

## 3. Approximate the null distribution

Our technique: simulate data from a world in which the null is true, then calculate the test statistic on the simulated data.

Which simulation method(s) align with the null hypothesis and our data collection process?

01:00

## Simulating the null using infer

library(tidyverse)
library(infer)

# update these based on the poll
n_k <- 40
n_b <- 20

shapes <- data.frame(name = c(rep("Kiki", n_k),
rep("Booba", n_b)))

shapes |>
specify(response = name,
success = "Kiki") |>
hypothesize(null = "point", p = .5) |>
generate(reps = 1, type = "draw") |>
calculate(stat = "prop")

## 4. Assess the consistency of the data and the null

null <- shapes |>
specify(response = name,
success = "Kiki") |>
hypothesize(null = "point", p = .5) |>
generate(reps = 500, type = "draw") |>
calculate(stat = "prop")

obs_p_hat <- shapes |>
specify(response = name,
success = "Kiki") |>
# hypothesize(null = "point", p = .5) |>
# generate(reps = 500, type = "simulate") |>
calculate(stat = "prop")

## 4. Assess the consistency of the data and the null

null |>
visualise() +

null |>
get_p_value(obs_p_hat, direction = "both")

## The p-value

What is the proper interpretation of this p-value?

01:00

# Break

05:00

# Problem Set 14: Hypothesis Testing II

50:00