Contingency tables summarize results where you compared two or more groups and the outcome is a categorical variable (such as disease vs. no disease, pass vs. fail, artery open vs. artery obstructed). Read elsewhere to learn about relative risks & odds ratios, sensitivity & specificity, and interpreting P values.
The results of a chi-square or Fisher's test only make sense if each subject (or experimental unit) is independent of the rest. That means that any factor that affects the outcome of one subject only affects that one subject. Prism cannot test this assumption. You must think about the experimental design. For example, suppose that the rows of the table represent two different kinds of preoperative antibiotics and the columns denote whether or not there was a postoperative infection. There are 100 subjects. These subjects are not independent if the table combines results from 50 subjects in one hospital with 50 subjects from another hospital. Any difference between hospitals, or the patient groups they serve, would affect half the subjects but not the other half. You do not have 100 independent observations. To analyze this kind of data, use the Mantel-Haenszel test or logistic regression. Beginning with version 8.3.0, Prism offers both simple logistic regression and multiple logistic regression.
In some experiments, subjects are matched for age and other variables. One subject in each pair receives one treatment while the other subject gets the other treatment. These data should be analyzed by special methods such as McNemar's test. Paired data should not be analyzed by chi-square or Fisher's test.
To be a true contingency table, each value must represent numbers of subjects (or experimental units). If it tabulates averages, percentages, ratios, normalized values, etc. then it is not a contingency table and the results of chi-square or Fisher's tests will not be meaningful. If you've entered observed values on one row (or column) and expected values on another, you do not have a contingency table, and should use a separate analysis designed for those kind of data.
The chi-square test is not only used for analyzing contingency tables. It can also be used to compare the observed number of subjects in each category with the number you expect to see based on theory. Prism cannot do this kind of chi-square test. It is not correct to enter observed values in one column and expected in another. When analyzing a contingency table with the chi-square test, Prism generates the expected values from the data – you do not enter them.
If your table has two columns and more than two rows (or two rows and more than two columns), Prism will perform the chi-square test for trend as well as the regular chi-square test. The results of the test for trend will only be meaningful if the rows (or columns) are arranged in a natural order, such as age, duration, or time. Otherwise, ignore the results of the chi-square test for trend and only consider the results of the regular chi-square test.