Statistics Comparisons
8 side-by-side comparisons of statistical methods, tests, and concepts with detailed analysis.
Z-Test vs T-Test
Z-Test vs T-Test
Two fundamental hypothesis tests for comparing means. The z-test assumes a known population standard deviation and large samples, while the t-test is designed for small samples or when the population standard deviation is unknown.
Type I vs Type II Error
Type I Error vs Type II Error
The two kinds of mistakes possible in hypothesis testing. A Type I error is a false positive (rejecting a true null hypothesis). A Type II error is a false negative (failing to reject a false null hypothesis).
Parametric vs Nonparametric Tests
Parametric Tests vs Nonparametric Tests
Two broad families of statistical tests. Parametric tests assume the data follow a specific distribution (usually normal) and operate on parameters like means. Nonparametric tests make fewer distributional assumptions and often use ranks.
Correlation vs Regression
Correlation vs Regression
Two related but distinct techniques for examining relationships between variables. Correlation measures the strength and direction of a linear association. Regression models the relationship and enables prediction of one variable from another.
Population vs Sample
Population vs Sample
The most foundational distinction in statistics. A population is the entire group you want to study. A sample is a subset selected from the population to draw conclusions about the whole.
One-Tailed vs Two-Tailed Tests
One-Tailed Test vs Two-Tailed Test
Two approaches to setting up the alternative hypothesis in hypothesis testing. A one-tailed test looks for an effect in a specific direction. A two-tailed test looks for any difference, regardless of direction.
Mean vs Median
Mean vs Median
Two primary measures of central tendency. The mean is the arithmetic average of all values. The median is the middle value when data are ordered, making it resistant to extreme observations.
ANOVA vs T-Test
ANOVA vs T-Test
Two hypothesis tests for comparing means. The t-test compares means of one or two groups. ANOVA (Analysis of Variance) extends this to three or more groups, controlling the overall Type I error rate.
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