| STATISTICAL PAGES |
|
| Year : 2015 | Volume
: 1
| Issue : 1 | Page : 69-71 |
|
|
Chi-square test and its application in hypothesis testing
Rakesh Rana, Richa Singhal
Statistical Section, Central Council for Research in Ayurvedic Sciences, Ministry of AYUSH, GOI, New Delhi, India
Correspondence Address:
Dr. Richa Singhal
Central Council for Research in Ayurvedic Sciences, Ministry of AYUSH, GOI, New Delhi
India
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/2395-5414.157577
|
|
|
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. |
|
|
|
| [FULL TEXT] [PDF]* |
|
 |
|
