Use of both parametric and non-parametric tests is based on the assumption that the sample has been ___ selected from the population. A nonparametric test is appropriate when the scores are ___ normally distributed.
randomly; not
Read: The randomized block design is used to control the effects of a (confounding) extraneous variable by ensuring that groups are equivalent with regard to that variable prior to the beginning of the study. It involves “blocking” (grouping) participants in terms of their status on that variable and then randomly assigning participants in each block to one of the treatment groups.
The Solomon four-group design is an experimental design that is used to evaluate the effects of pretesting on post-test scores, used to evaluate both internal and external validity.
The nonequivalent groups design is a quasi-experimental research design that is used when intact groups must be used (i.e., when participants cannot be randomly assigned to different treatment groups).
Read: The t-test is used to compare two means, and when one of the means is a sample mean and the other is a known population mean, the appropriate t-test is the single-sample t-test. The independent samples t-test is the appropriate t-test for comparing means obtained from two independent samples.
Chi-square tests are used to evaluate frequency of levels of a categorical variable. The single-sample chi-square test is used to compare the number (frequency) of cases in each category when a study includes a single variable. The multiple-sample chi-square test is used to compare the number (frequency) of cases in each category when a study includes two or more variables.
Read:
The homogeneity of variance assumption: different groups being compared in a statistical test (like ANOVA or t-tests) have roughly equal population variances, meaning the spread or dispersion of scores around the mean should be similar across groups
Equal-sized groups would help compensate for a violation of the assumption of homogeneity of variances
Read: A nonparametric test is a statistical hypothesis test that makes few or no assumptions about the underlying distribution of your data, unlike parametric tests (like t-tests or ANOVA) which assume normality. Also called “distribution-free tests,” they are ideal for skewed data, small sample sizes, ordinal data (ranks), or when outliers are present, often working with ranks or medians instead of means, though they typically have less statistical power than their parametric counterparts when assumptions are met.
A parametric test is a statistical method that assumes your sample data comes from a population following a specific probability distribution, most commonly the normal (bell-curve) distribution, and relies on population parameters like the mean and standard deviation, offering greater statistical power but requiring stricter data conditions (e.g., normality, equal variances) than non-parametric tests. Common examples include t-tests and ANOVA.
Read: ANOVA (Analysis of Variance) is used in experimental designs
The analysis of covariance (ANCOVA) is used to statistically remove the effects of an extraneous variable in an experimental design (C for confounding)
A randomized block ANOVA is also used to control the effects of an extraneous variable, but it requires that participants be “blocked” (grouped) in terms of that variable and participants in each block be randomly assigned to one of the interventions
A factorial ANOVA is appropriate when a study has two or more independent variables
The MANOVA is used to analyze the effects of one or more independent variables on TWO OR MORE DEPENDENT variables that are each measured on an interval or ratio scale. (M for multiple DVs)
The split-plot (mixed) ANOVA is the appropriate technique when at least one independent variable is a between-groups variable and another independent variable is a within-subjects variable.
Trend analysis is useful for determining the nature of the relationship between a quantitative IV and DV and indicates if there is a significant linear, quadratic, cubic, or quartic trend
