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Introduction to Hypothesis Testing - Coggle Diagram
Introduction to Hypothesis Testing
Hypothesis Testing - A statistical method that uses sample data to evaluate a hypothesis about a population.
State a hypothesis about a population.
Use the hypothesis to describe what values we should expect for the sample mean, if the hypothesis is really true.
Then, obtain a random sample from the population.
Compare the obtained sample data with the prediction that was made from the hypothesis.
Elements of Testing
Unknown Population
Sample
Terminology
The Null Hypothesis states that the treatment has no effect.
The Alternative (Scientific) Hypothesis states that there is a change, a difference, or a relationship for the general population.
Alpha Level - The "level of significance", is a probability value that is used to define the concept of “very unlikely” in a hypothesis test.
Test Statistics = Show that sample data are converted into a single, specific statistic that is used to test hypotheses.
Effect Size - intended to provide a measurement of the absolute magnitude of a treatment effect, independent of the size of the sample(s) being used.
Power - the probability that the test will correctly reject a false null hypothesis.
Errors
Type I Error
occurs when a researcher rejects a null hypothesis that is actually true. (Alpha)
A
Type II Error
occurs when a researcher fails to reject a null hypothesis that is in fact false. (Beta)
Assumptions (with Z-Scores)
Random Sampling
Independent Observations
Standard Error
Normal Sampling Distribution
Procedures
Nondirectional (two-tailed) - The critical region is divided between the two tails of the distribution. (COMMON)
Directional (one-tailed) - The statistical hypotheses specify either an increase or a decrease in the population mean. (UNCOMMON)