The Wilcoxon signed-rank test is a non-parametric statistical test used to compare two related samples or matched pairs to assess whether their population mean ranks differ. It is particularly useful when the data does not meet the assumptions of a parametric test like the paired t-test, such as normality or when dealing with ordinal data.

Wilcoxon Signed-rank Test

Wilcoxon signed-rank test

non-parametric statistical test

related samples

Matched pairs is a statistical technique used in experimental design to control for confounding variables by pairing participants with similar characteristics and randomly assigning them to different treatment groups. This method enhances the validity of causal inferences by ensuring that differences in outcomes are more likely due to the treatment rather than pre-existing differences between participants.

matched pairs

population mean ranks

A parametric test is a statistical test that makes assumptions about the parameters of the population distribution from which the sample is drawn, typically assuming a normal distribution. These tests are powerful and efficient when their assumptions are met, but can lead to incorrect conclusions if the assumptions are violated.

parametric test

A paired t-test is a statistical method used to compare two related groups to determine if their means differ significantly. It is commonly used when the same subjects are measured under two different conditions or at two different times, accounting for the natural pairing of observations.

paired t-test

Normality is a measure of concentration equivalent to molarity but accounts for the reactive capacity of a solute, often used in acid-base chemistry and redox reactions. It is defined as the number of equivalents of solute per liter of solution, where an equivalent is the amount of substance that reacts with or supplies one mole of hydrogen ions or electrons.

normality

Ordinal data represents categories with a meaningful order but without a uniform scale, allowing for the ranking of data points. Unlike interval data, ordinal data does not quantify the difference between categories, making it suitable for non-parametric statistical tests.

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