Open AccessChi-square test and its application in hypothesis testing
Author(s) -
Rakesh Rana,
Richa Singhal
Publication year - 2015
Publication title -
journal of the practice of cardiovascular sciences
Language(s) - English
Resource type - Journals
eISSN - 2454-2830
pISSN - 2395-5414
DOI - 10.4103/2395-5414.157577
Subject(s) - contingency table , categorical variable , chi square test , statistics , pearson's chi squared test , statistic , test statistic , test (biology) , calculator , null hypothesis , mathematics , p value , statistical hypothesis testing , table (database) , square (algebra) , computer science , arithmetic , data mining , paleontology , geometry , biology , operating system
In medical research, there are studies which often collect data on categorical variables that can be summarized as a series of counts. These counts are commonly arranged in a tabular format known as a contingency table. The chi-square test statistic can be used to evaluate whether there is an association between the rows and columns in a contingency table. More specifically, this statistic can be used to determine whether there is any difference between the study groups in the proportions of the risk factor of interest. Chi-square test and the logic of hypothesis testing were developed by Karl Pearson. This article describes in detail what is a chi-square test, on which type of data it is used, the assumptions associated with its application, how to manually calculate it and how to make use of an online calculator for calculating the Chi-square statistics and its associated P-value