Inferential Statistics Body of statistical computations relevant to making inferences from findings based on sample observations to some larger population Used for hypothesis testing

Types of Hypotheses Research (alternative) hypothesis: –States the expected relationship or difference between two or more variables –Published in reports/findings Null Hypothesis –Suggests there is no relationship among the variables under study –Null is statistically tested –Belief in the null hypothesis continues until there is sufficient evidence to the contrary

Hypothesis Testing/Significance Levels The researcher sets the significance level, or p, for each statistical test The degree of error the researcher finds acceptable in a statistical test Generally p<.05 is acceptable –5 out of 100 findings that appear to be valid will be due to chance –If p >.05, the finding is non-significant and null hypothesis is retained –If p <.05, the finding is significant, null hypothesis rejected

In reality, the null hypothesis is true In reality, the null hypothesis is false Use level of significance to reject null Type I error – Null is rejected even though it is true Decision 1 – Null is rejected when it is false Use level of significance to retain the null Decision 2 – Null is retained when it is true Type II error – Null is retained even though it is false