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Chapter 8: Hypothesis Testing - Coggle Diagram
Chapter 8: Hypothesis Testing
Steps of Hypothesis Testing
Step 1- statement of the hypothesis
Step 2- setting of the criteria for a decision
Step 3- collection of data and computation of sample statistics
Step 4- decision making
Hypothesis testing- statistical method that uses sample data to evaluate a supposition about a population
Alternative hypothesis- states that there is a change, a difference, or a relationship for the general population
Critical Region- group of extreme sample values very unlikely to be obtained if null hypothesis is true
Null hypothesis- states that in the general population there is no change, no difference, or no relationship
Alpha Level- probability value that is used to define the concept of �very unlikely�
Power- probability that the test will correctly reject a false null hypothesis
Types of Errors
Type II Error- occurs when a researcher fails to reject a null hypothesis that is in fact false
Beta- probability of a Type II error
Type I Error- occurs when a researcher rejects a null hypothesis that is actually true
Alpha level determines the probability of a Type I error.
Test Statistic- indicates that the sample data are converted into a single figure to test a hypothesis
Significant- result that is very unlikely to occur when the null hypothesis is true
Directional Hypothesis Test- method wherein statistical suppositions specify either an increase or a decrease in the population mean
Effect Size- measurement of the absolute magnitude of a treatment result
Cohen’s D- measure of the distance between two means, typically reported as a positive number