The Proportion Test computes a test statistic to determine if a specific category in a sample has a different proportion than the expected proportion, p_{0}. The node calculates the sample proportion of the category p̂. With the given null-hypothesized proportion p_{0}, a z-Score is calculated via (p̂ - p_{0})/*se*, where *se* is the standard error. If the corresponding *p*-Value is below the given significance level α, the null hypothesis can be rejected. Missing values will be ignored. See Wikipedia on test statistic, "One-proportion z-test" for further information.

- Category column
- The distribution column, of StringType or BooleanType. Its total number of occurrences without missing values is
*nobs*. - Category
- The category to test for. The number of occurrences of this category in the category column is
*count*. - Null hypothesis
- The null hypothesized proportion 0 < p
_{0}< 1. - Alternative hypothesis
- The alternative hypothesis H
_{A}: whether the actual proportion*p̂*=nobs/count is- larger than (GREATER_THAN)
- less than (LESS_THAN)
- different than (TWO_SIDED)

_{0}. - Significance level α
- H
_{0}is rejected, if*p*-Value < α

- Use sample proportion
- Whether to calculate the standard error from the sample proportion p̂ rather than the default null hypothesis proportion p
_{0}.

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v4.7

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