Please enable JavaScript.
Coggle requires JavaScript to display documents.
Hypothesis Testing: using sample data to evaluate a claim about a…
Hypothesis Testing: using sample data to evaluate a claim about a population
Hypotheses and Decisions
Critical Region: extreme sample outcomes that lead to rejecting the null hypothesis
Alternative Hypothesis (H₁): states that a treatment effect or population change exists
Test Statistic: a calculated value used to evaluate the null hypothesis
Alpha Level (α): the significance threshold that determines the risk of a Type I error
Null Hypothesis (H₀): states that there is no treatment effect or population change
Types of Hypothesis Tests
Two-Tailed Test: tests for differences in either direction, positive or negative
One-Tailed Test: tests for a difference in one predetermined direction
Directional Prediction: predicts whether a treatment will increase or decrease scores
Critical Value: the cutoff separating the critical region from other outcomes
Effect Size and Sample Characteristics
Standard Error: the standard deviation of the sampling distribution of a statistic
Effect Size: measures the magnitude of a treatment effect or difference
Variability: the spread of scores; greater variability makes effects harder to detect
Sample Size (n): the number of observations; larger samples generally improve the ability to detect effects
Cohen's d: a standardized measure of the difference between means
Statistical Power and Calculations
Statistical Power: the probability of correctly rejecting a false null hypothesis
z-Score: shows how many standard errors a sample mean is from the hypothesized population mean
Distribution of Sample Means: the distribution of means from all possible random samples of a given size
Treatment Effect: a change in the population outcome attributable to an intervention
p-Value: the probability (assuming H₀ is true) of obtaining a result at least as extreme as the observed one
Errors and Significance
Type II Error: failing to reject a false null hypothesis; a false negative
Beta (β): the probability of making a Type II error
Statistical Significance: a result unlikely enough under H₀ to justify rejecting it at the chosen alpha
Fail to Reject H₀: insufficient evidence to conclude that an effect exists
Type I Error: rejecting a true null hypothesis; a false positive