One Sample Proportion Test with Minitab

What is the One Sample Proportion Test?

One sample proportion test is a hypothesis test to compare the proportion of one certain outcome (e.g. the number of successes per the number of trials, or the number of defects per the total number of opportunities) occurring in a population following the binomial distribution with a specified proportion.

  • Null Hypothesis (H0): p = p0
  • Alternative Hypothesis (Ha): pp0

One Sample Proportion Test Assumptions

  • The sample data drawn from the population of interest are unbiased and representative.
  • There are only two possible outcomes in each trial: success/failure, yes/no, and defective/non-defective etc.
  • The underlying distribution of the population is binomial distribution.
  • When np ≥ 5 and np(1 – p) ≥ 5, the binomial distribution can be approximated by the normal distribution.

How the One Sample Proportion Test Works

When np ≥ 5 and np(1 – p) ≥ 5, we use normal distribution to approximate the underlying binomial distribution of the population.
Test Statistic


One Sample Proportion MTB_00

  • p ̂  is the observed probability of one certain outcome occurring
  • p0 is the hypothesized probability
  • n is the number of trials.
  • When |Zcalc| (absolute value of the calculated test statistic) is smaller than Zcrit (critical value), we fail to reject the null hypothesis and claim that there is no statistically significant difference between the population proportion and the hypothesized proportion.

Use Minitab to Run a One Sample Proportion Test

Case study: We are interested in comparing the exam pass rate of a high school this month against a specified rate (70%) using a nonparametric (i.e. distribution-free) hypothesis test: one sample proportion test.
Data File: “One Sample Proportion” tab in “Sample Data.xlsx”

  • Null Hypothesis (H0): p = 70%
  • Alternative Hypothesis (Ha): p ≠ 70%

Steps to run a one sample proportion test in Minitab:

  1. Click Stat → Basic Statistics → 1 Proportion.
  2. A new window named “One-SampleProportion” appears.
  3. Click on the drop-down menu and choose “Summarized data.”
  4. Enter “77” in the box of “Number of events.”
  5. Enter “105” in the box of “Number of trials.”
  6. Check the box “Perform hypothesis test.”
  7. Enter “0.70” as the “Hypothesized proportion.”
  8. Click “OK.”
  9. The one sample proportion test results appear in the session window.

Model summary: The p-value of the one sample proportion test is 0.460, greater than the alpha level (0.05), and we fail to reject the null hypothesis. We conclude that the exam pass rate of the high school this month is not statistically different from 70%.



About Michael Parker

Michael Parker is the President and CEO of the Lean Sigma Corporation, a management consulting firm and online six sigma training, certification, and courseware provider. Michael has over 25 years of experience leading and executing lean six sigma programs and projects. As a Fortune 50 senior executive, Michael led oversight of project portfolios as large as 150 concurrent projects exceeding $100 million in annual capital expenditures. Michael has also managed multi-site operations with the accountability of over 250 quality assurance managers, analysts, and consultants. He is an economist by education, earning his Bachelor of Science degree from Radford University while also lettering four years as an NCAA Division I scholarship athlete. Michael earned his Six Sigma Master Black Belt certification from Bank of America and his Black Belt certification from R.R. Donnelley & Sons.