| 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 |
 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 ) |
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 |
