Binomial test

The binomial test is a hypothesis test used when there is a categorical variable with two expressions, e.g., gender with male and female. The binomial test can then check whether the frequency distribution of the variable corresponds to an expected distribution, e.g.

• Men and women occur equally often.
• The share of women is 54 %.

This is a special case when it is to be tested in general whether the frequency distribution of the variables has arisen by chance or not. In this case, the probability of occurrence is set to 50%.

The binomial test can therefore be used to check whether the frequency distribution of a sample matches that of the population or not.

Hypotheses in binomial test

The hypothesis at the binomial test result in the one sided case to

• Null hypothesis: The frequency distribution of the sample corresponds to the distribution of the population.
• Alternative hypothesis: The frequency distribution of the sample does not correspond to the distribution of the population.

Thus, the undirected hypothesis only tests whether there is a difference or not, but not in which direction this difference goes.

In the two sided case, the aim is to investigate whether the probability of occurrence of an expression in the sample is greater or less than a given or true percentage.

In this case, an expression is defined as "success" and it is checked whether the true "probability of success" is smaller or larger than that in the sample.

The alternative hypothesis then results in:

• Alternative hypothesis: True probability of success is smaller/larger than specified value

Calculate binomial test

To calculate a binomial test, you need the sample size, the number of cases that are positive of it, and the probability of occurrence in the population.

Example Binomial test

A possible example for a binomial test would be the question whether the gender ratio in the specialization marketing at the university XY differs significantly from that of all business students at the university XY (population).

Listed below are the students majoring in marketing; women make up 55% of the total business degree program.

DATAtab gives you the following result for this example data:

Binomialtest interpretieren

With an expected test value of 55%, the p-value is 0.528. This means that the p-value is above the alpha level of 5% and the result is therefore not significant. Consequently, the null hypothesis must be retained. In terms of content, this means that the gender ratio of the marketing specialization (=sample) does not differ significantly from that of all business administration students at XY University (=population).