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Table 1 Some commonly used statistical tests

Parametric test Example of equivalent non-parametric test Purpose of test Example

Two sample (unpaired) t test Mann-Whitney U test Compares two independent samples drawn from the same population To compare girls' heights with boys' heights
One sample (paired) t test Wilcoxon matched pairs test Compares two sets of observations on a single sample To compare weight of infants before and after a feed
One way analysis of variance (F test) using total sum of squares Kruskall-Wallis analysis of variance by ranks Effectively, a generalisation of the paired t or Wilcoxon matched pairs test where three or more sets of observations are made on a single sample To determine whether plasma glucose level is higher one hour, two hours, or three hours after a meal
Two way analysis of variance Two way analysis of variance by ranks As above, but tests the influence (and interaction) of two different covariates In the above example, to determine if the results differ in male and female subjects
{chi}2 test Fisher's exact test Tests the null hypothesis that the distribution of a discontinuous variable is the same in two (or more) independent samples To assess whether acceptance into medical school is more likely if the applicant was born in Britain
Product moment correlation coefficient (Pearson's r) Spearman's rank correlation coefficient (r{sigma}) Assesses the strength of the straight line association between two continuous variables. To assess whether and to what extent plasma HbA1 concentration is related to plasma triglyceride concentration in diabetic patients
Regression by least squares method Non-parametric regression (various tests) Describes the numerical relation between two quantitative variables, allowing one value to be predicted from the other To see how peak expiratory flow rate varies with height
Multiple regression by least squares method Non-parametric regression (various tests) Describes the numerical relation between a dependent variable and several predictor variables (covariates) To determine whether and to what extent a person's age, body fat, and sodium intake determine their blood pressure




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