Comparison of Two Groups

This set of flashcards covers key vocabulary related to the comparison of two groups in statistical analysis.

Comparison of Two Groups

Analyzing differences between two groups in terms of means for quantitative data or proportions for categorical data.

Dependent Samples

Samples where the same subjects are measured at two or more points in time, allowing for paired analyses.

Independent Samples

Samples where subjects in one sample are completely different from those in the other, with no natural matching.

Response Variable

The outcome variable that comparisons focus on; this is the variable being measured.

Explanatory Variable

The variable that defines the groups being compared.

Standard Error

An estimate of the variability of a sampling statistic, which measures how accurately the sample statistic estimates the population parameter.

Confidence Interval

A range of values estimated from a data set that is likely to contain the population parameter with a specified level of confidence.

P-value

The probability of obtaining the observed data, or something more extreme, when the null hypothesis is true; used to determine significance.

Null Hypothesis (Ho)

A statement that there is no effect or difference, often tested to determine if there is sufficient evidence to reject it.

Alternative Hypothesis (Ha)

A statement that indicates the presence of an effect or difference, opposing the null hypothesis.

McNemar's Test

A statistical test used to compare paired proportions in a contingency table, particularly for dependent samples.

Wilcoxon-Mann-Whitney Test

A non-parametric test for comparing two independent samples; used when the assumptions for t-tests are not met.

Effect Size

A quantitative measure of the magnitude of a phenomenon; often calculated as the difference between two means divided by their pooled standard deviation.

Relative Risk

The ratio of the probability of an event occurring in an exposed group versus a control (non-exposed) group.

Significance Testing

A statistical method for determining if the observed effects in data are due to chance or represent actual differences.

Random Sampling

A sampling method in which every individual has an equal chance of being selected, ensuring the sample is representative of the population.

Cross-sectional Studies

Observational studies that analyze data from a population at a specific point in time.

Longitudinal Studies

Research studies that follow subjects over time, allowing for measurement of change.

Binary Variable

A variable that has only two categories or levels, often used in comparisons between groups.

Sample Size (n)

The number of observations in a study, which affects the power and validity of statistical tests.

Variance

A measure of how much values in a data set differ from the mean; key for calculating the standard error.

Normal Distribution

A bell-shaped distribution that is symmetrical about the mean, significant in the context of many statistical tests.

Sampling Distribution

The probability distribution of a statistic obtained from a sample, showing how the statistic varies from sample to sample.

Standard Deviation (s)

A measure of the amount of variation or dispersion in a set of values; used alongside mean to understand data distributions.

Bivariate Analysis

Statistical analysis that involves two variables to determine relationships or comparisons between them.