Hypothesis Testing Overview
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Questions and Answers

What is hypothesis testing?

A process used to quantify belief against a particular hypothesis about a population.

Which step is NOT part of hypothesis testing?

  • Calculate the mean of the sample (correct)
  • Compute the p-value
  • Choose the level of significance
  • Define the null and alternative hypotheses

The null hypothesis assumes that there is a difference between the groups.

False (B)

What is an alpha error?

<p>The probability of erroneously rejecting the null hypothesis when it is actually true.</p> Signup and view all the answers

What is a beta error?

<p>The probability of wrongly accepting the null hypothesis when it is actually false.</p> Signup and view all the answers

What does a p-value less than 0.05 indicate?

<p>Reject the null hypothesis (B)</p> Signup and view all the answers

What does p-value represent?

<p>The probability of observing the effect seen in the study if the null hypothesis is true.</p> Signup and view all the answers

The null hypothesis (Ho) states that no ______ exist between groups.

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

Match the following terms with their definitions:

<p>Null Hypothesis = Assumes no difference between groups Alternative Hypothesis = States there is a difference between groups Type I Error = Rejecting the null hypothesis when true Type II Error = Accepting the null hypothesis when false</p> Signup and view all the answers

Study Notes

Hypothesis Testing

  • Hypothesis testing is used to quantify belief against a particular hypothesis.
  • It determines if there is sufficient evidence to reject a specific hypothesis.
  • The null hypothesis assumes no difference between groups.
  • The alternative hypothesis suggests there is a difference between groups.

Steps in Hypothesis Testing

  • Define the null and alternative hypotheses.
  • Choose the level of significance (usually 0.05 or less).
  • Select and compute the test statistic (e.g., t-test, chi-square).
  • Calculate the p-value.
  • Compare the p-value to the level of significance to determine whether to reject the null hypothesis.
  • Draw conclusions from the test.

Types of Errors in Research

  • Type I error (α error): Incorrectly rejecting the null hypothesis when it is true (false positive).
  • Type II error (β error): Incorrectly failing to reject the null hypothesis when it is false (false negative).

Probability Value (p-value)

  • The p-value represents the probability of observing the obtained effect (or a stronger effect) if the null hypothesis is true.
  • It indicates the likelihood of the observed difference occurring by chance.
  • A p-value of less than 0.05 is typically used as a cutoff for rejecting the null hypothesis.

Interpretation of Results

  • If the p-value is less than 0.05, reject the null hypothesis and accept the alternative hypothesis.
  • If the p-value is greater than 0.05, accept the null hypothesis and reject the alternative hypothesis.

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Description

This quiz explores the fundamental concepts of hypothesis testing, including the definitions of null and alternative hypotheses. You'll learn about the significance levels, test statistics, and common errors in hypothesis testing. Test your understanding of p-values and their importance in making decisions within research.

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