Experimental Design and Data Analysis Quiz

Control group ensures a baseline for comparing experimental results, isolating the effect of the independent variable.

Variance is a measure of data spread or dispersion, not central tendency; mean, median, and mode are central tendency measures.

Histograms visually represent the distribution of continuous variables, highlighting patterns and central tendencies.

Scatter plots provide a visual representation of the relationship between two continuous variables, revealing patterns and correlations.

Standard deviation measures the spread or dispersion of data points around the mean in a data set.

Independent samples t-test is suitable for comparing means of two groups with independent observations.

P-value indicates the likelihood of observing data if the null hypothesis is accurate; lower values suggest stronger evidence against the null hypothesis.

Blinding minimizes researcher bias by keeping participants or assessors unaware of treatment conditions.

Randomization ensures equal assignment chances, reducing bias and confounding variables in experimental groups.

Pilot studies gather initial data to refine experimental design, identify potential issues, and improve study precision.

Type II error occurs when the null hypothesis is true, but the test fails to reject it; accepting a false null hypothesis.

Experimental studies, with manipulation of variables, are best for establishing causality and identifying cause-and-effect relationships.

Factorial design manipulates multiple independent variables, and the number varies based on the specific study.