Please enable JavaScript.
Coggle requires JavaScript to display documents.
Chapter 8 Introduction to Hypothesis Testing - Coggle Diagram
Chapter 8 Introduction to Hypothesis Testing
Step 2: setting the criteria
Step 3: collection of data and computation of sample statistics
Step 1: statement of the hypothesis
Step 4: decision making
null hypothesis: states that in the general population there is no change, no difference, or no relationship
alternative hypothesis: there is a change, difference, or a relationship for the general population.
The four steps of a hypothesis test
hypothesis testing: statistical method that uses sample data to evaluate a supposition about a population
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.
A hypothesis test leads to one of two decisions: the sample data provide sufficient evidence to reject the null hypothesis and conclude the treatment has an effect and/or the sample data do not provide enough evidence to reject the null hypothesis.
Cohen's d - measure of the distance between two means, typically reported as a positive number.
power: probability that the test will correctly reject a false null hypothesis
effect size: measurement of the absolute magnitude of a treatment result.
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.
critical region: group of extreme sample values very unlikely to be obtained if null hypothesis is true.
test statistic: indicated that the sample data are converted into a single figure to test a hypothesis