# Binomial test for a single proportion

This page offers all the basic information you need about the binomial test for a single proportion. It is part of Statkat’s wiki module, containing similarly structured info pages for many different statistical methods. The info pages give information about null and alternative hypotheses, assumptions, test statistics and confidence intervals, how to find *p * values, SPSS how-to’s and more.

To compare the binomial test for a single proportion with other statistical methods, go to Statkat's or practice with the binomial test for a single proportion at Statkat's

##### Contents

- 1. When to use
- 2. Null hypothesis
- 3. Alternative hypothesis
- 4. Assumptions
- 5. Test statistic
- 6. Sampling distribution
- 7. Significant?
- 8. Example context
- 9. SPSS
- 10. Jamovi

##### When to use?

Deciding which statistical method to use to analyze your data can be a challenging task. Whether a statistical method is appropriate for your data is partly determined by the measurement level of your variables. The binomial test for a single proportion requires one variable of the following type:

One categorical with 2 independent groups |

Note that theoretically, it is always possible to 'downgrade' the measurement level of a variable. For instance, a test that can be performed on a variable of ordinal measurement level can also be performed on a variable of interval measurement level, in which case the interval variable is downgraded to an ordinal variable. However, downgrading the measurement level of variables is generally a bad idea since it means you are throwing away important information in your data (an exception is the downgrade from ratio to interval level, which is generally irrelevant in data analysis).

If you are not sure which method you should use, you might like the assistance of our method selection tool or our method selection table.

##### Null hypothesis

The binomial test for a single proportion tests the following null hypothesis (H_{0}):

_{0}: $\pi = \pi_0$

$\pi$ is the population proportion of 'successes'; $\pi_0$ is the population proportion of successes according to the null hypothesis

##### Alternative hypothesis

The binomial test for a single proportion tests the above null hypothesis against the following alternative hypothesis (H_{1} or H_{a}):

_{1}two sided: $\pi \neq \pi_0$

H

_{1}right sided: $\pi > \pi_0$

H

_{1}left sided: $\pi < \pi_0$

##### Assumptions

Statistical tests always make assumptions about the sampling procedure that's been used to obtain the sample data. So called parametric tests also make assumptions about how data are distributed in the population. Non-parametric tests are more 'robust' and make no or less strict assumptions about population distributions, but are generally less powerful. Violation of assumptions may render the outcome of statistical tests useless, although violation of some assumptions (e.g. independence assumptions) are generally more problematic than violation of other assumptions (e.g. normality assumptions in combination with large samples).

The binomial test for a single proportion makes the following assumptions:

- Sample is a simple random sample from the population. That is, observations are independent of one another

##### Test statistic

The binomial test for a single proportion is based on the following test statistic:

$X$ = number of successes in the sample##### Sampling distribution

Sampling distribution of $X$ if H0 were true:Binomial($n$, $P$) distribution.

Here $n = N$ (total sample size), and $P = \pi_0$ (population proportion according to the null hypothesis).

##### Significant?

This is how you find out if your test result is significant:

Two sided:- Check if $X$ observed in sample is in the rejection region or
- Find two sided $p$ value corresponding to observed $X$ and check if it is equal to or smaller than $\alpha$

- Check if $X$ observed in sample is in the rejection region or
- Find right sided $p$ value corresponding to observed $X$ and check if it is equal to or smaller than $\alpha$

- Check if $X$ observed in sample is in the rejection region or
- Find left sided $p$ value corresponding to observed $X$ and check if it is equal to or smaller than $\alpha$

##### Example context

The binomial test for a single proportion could for instance be used to answer the question:

Is the proportion of smokers amongst office workers different from $\pi_0 = .2$?##### SPSS

How to perform the binomial test for a single proportion in SPSS:

Analyze > Nonparametric Tests > Legacy Dialogs > Binomial...- Put your dichotomous variable in the box below Test Variable List
- Fill in the value for $\pi_0$ in the box next to Test Proportion

##### Jamovi

How to perform the binomial test for a single proportion in jamovi:

Frequencies > 2 Outcomes - Binomial test- Put your dichotomous variable in the white box at the right
- Fill in the value for $\pi_0$ in the box next to Test value
- Under Hypothesis, select your alternative hypothesis