# 8.1.2.2.2.1 - Minitab Example: Normal Approx. Method

8.1.2.2.2.1 - Minitab Example: Normal Approx. Method## Example: Gym membership

**Research question: **Are less than 50% of all individuals with a membership at one gym female?

A simple random sample of 60 individuals with a membership at one gym was collected. Each individual's biological sex was recorded. There were 24 females.

First we have to check the assumptions:

np = 60 (0.50) = 30

n(1-p) = 60(1-0.50) = 30

The assumptions are met to use the normal approximation method.

To perform a one sample proportion *z* test with summarized data in Minitab:

- In Minitab, select
*Stat > Basic Statistics > 1 Proportion* - Select
*Summarized data*from the dropdown - For number of events, add 24 and for number of trials add 60.
- Check the box next to
*Perform hypothesis*test and enter*0.50*in the*Hypothesized proportion*box - Select
*Options* - Use the default
*Alternative hypothesis*setting of*Proportion < hypothesized**proportion*value - Use the default
*Confidence level*of 95 - Select
*Normal approximation method* - Click OK and OK

The result should be the following output:

#### Method

Event: Event proportion

Normal approximation is used for this analysis.

N | Event | Sample p | 95% Upper Bound for p |
---|---|---|---|

60 | 24 | 0.400000 | 0.504030 |

Null hypothesis | H _{0}: p = 0.5 |
---|---|

Alternative hypothesis | H _{1}: p < 0.5 |

Z-Value | P-Value |
---|---|

-1.55 | 0.061 |

We could summarize these results using the five-step hypothesis testing procedure:

\(np_0=60(0.50)=30\) and \(n(1-p_0)=60(1-0.50)=30\) both values are at least 10 so we can use the normal approximation method.

\(H_0\colon p = 0.50\)

\(H_a\colon p < 0.50\)

From output, \(z\) = -1.55

From output, \(p\) = 0.061

\(p \geq \alpha\), fail to reject the null hypothesis

There is not enough evidence to support the alternative that the proportion of women memberships at this gym is less than 50%.