I received an email from India (isn't the Internet wonderful?) that asked me to comment on the differences between a Chi-square test, Fisher's Exact test, and logistic regression. Let's take each of these in sequence.

A Chi-square test can mean a variety of different things depending on the context of the problem. I outline some of these in

For the record, there is no standard spelling for this. The terms "Chi square" and "Chi squared" are both acceptable, and you can also hyphenate the two words (Chi-square) or make them a single word (Chisquare). You can also use an initial capital letter or lower case for "Chi" and/or "Square". I use different versions throughout my web pages. Two quotes about consistency (found at en.thinkexist.com) capture my feelings on the topic:

*Consistency is the last refuge of the unimaginative*. Oscar Wilde, and*A foolish consistency is the hobgoblin of little minds, adored by little statesmen and philosophers and divines*. Ralph Waldo Emerson.

In the context of the original email, the Chi-square test was intended to compare the proportion of patients who die under two separate surgical procedures. This produces a two-by-two table. I outline some of the data entry issues associated with two-by-two tables at

and discuss some of the alternative statistical summaries (odds ratio, relative risk, number needed to treat) at

The Chi-square test has some flexibility in that if there are more than two rows or more than two columns in your table, you can still use this test. Beware, though, that the Chi-square test can be inefficient if the variable representing multiple rows or multiple columns is ordinal rather than nominal.

Fisher's Exact test is an alternative to the Chi-square test for two-by-two tables. The adjective "exact" stresses that this test does not rely on an approximation. Although some have cautioned about the conservative nature of Fisher's Exact test, there is general consensus in the research community that this test is preferred to the Chi-square test for small data sets, though there are varying rules for deciding whether a data set is small enough to warrant the use of Fisher's Exact test.

There used to be some reluctance to use Fisher's Exact test for moderate size data sets because the calculations needed for this test increase rapidly as the sample size increases. But with recent improvements in the computational algorithms, and with the ever increasing speed and power of computers, there is no reason to avoid this test, except, perhaps, for the very largest of data sets.

There are extensions of Fisher's Exact test to situations where there are more than two rows/columns, but these extensions are not available in most statistical software packages.

I discuss the use of Fisher's Exact test including some examples of how to report the results in a publication at

A simple logistic regression model will produce results that are very similar to the Chi-square test and Fisher's Exact test. A more complex logistic model, though, such as one that includes additional covariates, may produce radically different results than either of these tests. The reason for this is that Fisher's Exact test cannot adjust for possible confounding variables.

Logistic regression is also preferred when you are trying to predict a binary outcome using a continuous predictor variable. Logistic regression uses odds ratios as a measure of the relationship between the outcome variable and the predictor variable. I discuss what this means and how to interpret results from a logistic regression model at

- Stats: The concepts behind the logistic regression model,
- Stats: Interactions in logistic regression (April 8, 2004)
- Stats: Categorical variables in a logistic regression model (June 1, 2004)
- Stats: Guidelines for logistic regression models, and
- Stats: SPSS dialog boxes for logistic regression.

I also have a cautionary tale about overfitting in the context of a logistic regression model: