A finding is statistically significant if the data differ from what we would expect by chance if there were, in fact, no actual difference. A parametric test is an inferential statistical analysis based on a set of assumptions about the population. A nonparametric test is an inferential statistical analysis that is not based on a set of assumptions about the population. A robust hypothesis test is one that produces fairly accurate results even when the data suggest that the population might not meet some of the assumptions. A two-tailed test is a hypothesis test in which the research hypothesis does not indicate a direction of the mean difference or change in the dependent variable, but merely indicates that there will be a mean difference. A one-tailed test is a hypothesis test in which the research hypothesis is directional, positing either a mean decrease or a mean increase in the dependent variable, but not both, as a result of the independent variable. A critical value is a test statistic value beyond which we reject the null hypothesis; often called a cutoff. The critical region is the area in the tails of the comparison distribution in which the null hypothesis can be rejected. An assumption is a characteristic that we ideally require the population from which we are sampling to have so that we can make accurate inferences. The probability used to determine the critical values, or cutoffs, in hypothesis testing is a p level (often called alpha). |