Statistical Inference and Experimental Design Quiz

Statistical inference involves making predictions or decisions about a population based on a limited sample of data.

Randomization ensures that each experimental condition has an equal chance of being assigned, minimizing bias and confounding variables.

Multicollinearity is not an assumption but rather a potential issue in linear regression, affecting the stability of coefficient estimates.

A control group serves as a baseline to assess the impact of the treatment by providing a reference for comparison.

The independent samples t-test assesses whether the means of two independent groups are significantly different from each other.

A Type II error occurs when the null hypothesis is false, but the test fails to reject it, leading to a missed detection of a true effect.

The p-value quantifies the evidence against the null hypothesis, indicating the probability of obtaining the observed data if the null hypothesis is true.

Power analysis helps in determining the required sample size to achieve sufficient statistical power to detect a meaningful effect.

The central limit theorem states that, as the sample size increases, the distribution of the sample mean approaches a normal distribution.

Factorial design allows researchers to investigate the impact of multiple independent variables on the dependent variable in a single study.

Crossover design involves exposing each participant to multiple treatments in a random order, allowing within-subject comparisons.

Interaction terms in regression help assess whether the relationship between two variables varies based on the levels of a third variable.