You can do exact multinomial tests of goodness-of-fit when the nominal variable has more than two values. If the total number of observations is too high (around a thousand), computers may not be able to do the calculations for the exact test, and you should use a G–test or chi-square test of goodness-of-fit instead (and they will give almost exactly the same result).
You compare the observed data with the expected data, which are some kind of theoretical expectation (such as a 1:1 sex ratio or a 3:1 ratio in a genetic cross) that you determined before you collected the data. The most common use is a nominal variable with only two values (such as male or female, left or right, green or yellow), in which case the test may be called the exact binomial test. You use the exact test of goodness-of-fit when you have one nominal variable. This is because the predictions of the null hypothesis are so simple that the probabilities can easily be calculated. There is no test statistic instead, you directly calculate the probability of obtaining the observed data under the null hypothesis. This is the P value.Įxact tests, such as the exact test of goodness-of-fit, are different.
Most statistical tests take the following form: The main goal of a statistical test is to answer the question, "What is the probability of getting a result like my observed data, if the null hypothesis were true?" If it is very unlikely to get the observed data under the null hypothesis, you reject the null hypothesis. You use the exact test of goodness-of-fit when you have one nominal variable, you want to see whether the number of observations in each category fits a theoretical expectation, and the sample size is small.
0 kommentar(er)
