K.I Egbuchulem
Division of Paediatric Surgery, Department of Surgery, University College Hospital, Ibadan
Introduction
The logic of hypothesis testing was first scripted by Karl Pearson (1857–1936), a renaissance scientist, in Victorian London, and in 1900 he published his manuscript in the philosophical magazine to elaborate the invention of Chi square distribution and goodness of fit test.1
Pearson’s Chi square distribution and the Chi square test, also known as test for goodness of fit, and test of independence, are his most important contribution to the modern theory of statistics since the early 20th century.1
The Chi-square test of independence (also known as the Karl Pearson Chi-square test, or simply the Chi-square) is one of the most useful statistics for testing hypotheses when the variables are nominal, as it often happens in clinical research.1
The Chi-square test is based on the Chi-square (2) distribution. which is roughly skewed, and it is a normal approximation of a binomial distribution as against the Fischer’s exact test, which is more exact.2 Chi square distribution tables are available from a variety of sources they can easily be found online or in scientific documents, and statistics textbooks.2
Also, the concept of degree of freedom is also employed in calculating 2. The Chi-square test is a significance test statistic, and should be followed with a strength statistic such as the Confidence interval, Cramer’s V when a significant Chi-square result has been obtained.4
The Chi-square is a non-parametric test statistic, also called a distribution-free test designed to analyse group differences when the dependent variable is measured at a nominal level. Like all non-parametric statistics, the Chi-square is robust with respect to the distribution of the data and the parametric equivalent is the Z-test, which is suited for smaller sample size less than 30. The Chi-square test does not require equality of variances among the study groups as seen in analysis of variance (ANOVA)
Unlike most statistics, the Chi-square (2) permits evaluation of both dichotomous independent variables, and multiple group studies and can provide considerable information about how each of the groups performed in the study. It also gives information not only on the significance of any observed differences, but also provides detailed information on exactly which categories account for any differences found.3 Thus, the amount and richness of the detailed information this statistic can provide renders it one of the most useful tools in the researcher’s array of available analytic tools, hence the libero or midfielder role Chi-square test plays in biomedical research.