1. Null Hypothesis (H₀): This represents the "no effect" scenario, assuming no significant difference
between groups or no relationship between variables.
2. Alternative Hypothesis (H₁): The opposite of the null hypothesis, proposing the actual effect you're
interested in finding.
Two types of alternative hypotheses:
oOne-tailed test: This specifies the direction of the expected effect (greater than or less
than).
oTwo-tailed test: This is non-directional, expecting a difference but not specifying the
direction.
3. Level of Significance (alpha): This is the probability of rejecting the null hypothesis when it's
actually true (type I error). Common significance levels are 0.05 or 0.01.
4. Test Distribution Method: Statistical tests rely on probability distributions to assess the likelihood of
observing your data under the null hypothesis. Different tests use different distributions:
Dependent/Independent Samples T-test: Compares means between groups (dependent for paired
samples, independent for separate groups).
One-way ANOVA: Compares means between more than two groups.
Pearson r: Measures the strength and direction of the linear relationship between two continuous
variables.
Chi-square test: Assesses the relationship between categorical variables.
5. P-value: The probability of observing your data or something more extreme, assuming the null
hypothesis is true. A low p-value (less than alpha) suggests rejecting the null hypothesis.
6. Statistical Decision: Based on the p-value, you make a statistical decision:
Reject H₀ (if p-value < alpha): Suggests evidence against the null hypothesis, supporting the
alternative hypothesis.
Fail to reject H₀ (if p-value > alpha): Inconclusive evidence, but doesn't necessarily confirm the null
hypothesis.
7. Conclusion: After analyzing the data and making a statistical decision, you interpret the results in
the context of your research question.
