Lecture 15: Two Sample Inference and Chi-Squared Procedures

0.0(0)
Studied by 0 people
call kaiCall Kai
learnLearn
examPractice Test
spaced repetitionSpaced Repetition
heart puzzleMatch
flashcardsFlashcards
GameKnowt Play
Card Sorting

1/7

flashcard set

Earn XP

Description and Tags

Flashcards containing key terms and definitions related to two-sample inference, hypothesis testing, and chi-squared procedures.

Last updated 8:20 PM on 4/23/26
Name
Mastery
Learn
Test
Matching
Spaced
Call with Kai

No analytics yet

Send a link to your students to track their progress

8 Terms

1
New cards

In order for a p-value to be considered significant, it must be less than a certain threshold. This threshold is…

a. 0.05 b. 0.1 c. 0.001

2
New cards

The effect size is an important measure for determining the strength of a relationship. It is measured by…

a. Cohen's d b. t-value c. r-squared

3
New cards

A confidence interval gives us information about the…

a. range of values that estimate the population parameter b. exact value of the population parameter c. variance of the sample data

4
New cards

For a two-proportion z-test, the null hypothesis states that…

a. there is no difference between the two population proportions b. at least one population proportion is greater c. both proportions are equal to zero

5
New cards

Part 3: True or False: The last few questions of the exam are Independent True/False choice items. Circle the correct answer.

All p-values derived from hypothesis tests should be reported in a consistent format.

a. True b. False

6
New cards

The central limit theorem states that as the sample size increases, the sampling distribution of the mean will approach…

a. a normal distribution b. a uniform distribution c. a binomial distribution

7
New cards

If the sample size is increased, the standard error will…

a. increase b. decrease c. remain the same

8
New cards

In hypothesis testing, a Type I error occurs when…

a. we reject the null hypothesis when it is actually true b. we fail to reject the null hypothesis when it is actually false c. we incorrectly fail to accept the alternative hypothesis