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5 Questions

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Hypothesis Testing: t-tests

Created by

@VirtuousMagnolia

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Questions and Answers

What is the purpose of conducting a t-test in hypothesis testing?

To analyze the variance within a sample.

To compare the means of one or two samples. (correct)

To assess the distribution of a population.

To determine the causal relationship between two variables.

Which term describes the outcome that is unlikely to happen by chance alone in hypothesis testing?

Sample

Probability

Statistical significance (correct)

Variance

In the context of hypothesis testing, what does the Central Limit Theorem (CLT) imply?

Sample means will approximate a normal distribution as sample size increases. (correct)

All population distributions are normal regardless of sample size.

Sample sizes must be greater than 100 for accurate results.

Variability decreases with increasing sample size.

What is the primary difference between a one-sample t-test and a two-sample independent t-test?

<p>One tests means from two groups while the other tests one group's mean against a known value.</p> Signup and view all the answers

What concept is crucial to assess the likelihood of observing data, if the null hypothesis is true?

<p>Probability</p> Signup and view all the answers

Study Notes

Hypothesis Testing: t-tests

This is a course about performing and interpreting the results of one sample and two-sample independent t-tests.
The key competency is to be able to perform and interpret results of one sample and two-sample independent t-tests.
The course will focus on essential concepts like:
- Sample
- Same
- Different
- Chance
- Probability
- Significance
- Statistical significance
Prior knowledge of the central limit theorem (CLT) is required.
- This knowledge is used when the population variance is known.
- The course will explore what to do when the population variance is unknown.

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Description

This quiz covers the essential concepts of performing and interpreting one-sample and two-sample independent t-tests. You'll explore key topics such as statistical significance and the role of chance and probability in hypothesis testing. Prior knowledge of the central limit theorem is essential for a comprehensive understanding of the material.