Estimation and tolerance intervals

Use to determine the sample size required to achieve a specific margin of error for confidence intervals or tolerance intervals. Conversely, you can estimate the margin of error based on sample size.

Sample size for estimation
Calculate the sample size required to estimate a parameter within a specified margin of error. In Minitab, choose Stat > Power and Sample Size > Sample Size for Estimation
Sample size for tolerance intervals
Calculate the sample size required to control the coverage of a tolerance interval using a specified margin of error. In Minitab, choose Stat > Power and Sample Size > Sample Size for Tolerance Intervals

Hypothesis tests

Use hypothesis tests to determine whether a population parameter differs from either a specified value or a different population parameter.

1-sample Z

Examine the relationship between power, sample size, and difference for a 1-sample Z-test. In Minitab, choose Stat > Power and Sample Size > 1-Sample Z.

Use a 1-sample z-test when you know the standard deviation of the population.

1-sample t

Examine the relationship between power, sample size, and difference for a 1-sample t-test. In Minitab, choose Stat > Power and Sample Size > 1-Sample t.

2-sample t

Examine the relationship between power, sample size, and difference for a 2-sample t-test. In Minitab, choose Stat > Power and Sample Size > 2-Sample t.

Paired t

Examine the relationship between power, sample size, and difference for a paired t-test. In Minitab, choose Stat > Power and Sample Size > Paired t.

1 proportion

Examine the relationship between power, sample size, and comparison proportion for a 1 proportion test. In Minitab, choose Stat > Power and Sample Size > 1 Proportion.

2 proportions

Examine the relationship between power, sample size, and comparison proportion for a 2 proportions test. In Minitab, choose Stat > Power and Sample Size > 2 Proportions.

1-sample Poisson rate

Examine the relationship between power, sample size, and comparison rate for a 1-sample Poisson rate test. In Minitab, choose Stat > Power and Sample Size > 1-Sample Poisson Rate.

2-sample Poisson rate

Examine the relationship between power, sample size, and comparison rate for a 2-sample Poisson rate test. In Minitab, choose Stat > Power and Sample Size > 2-Sample Poisson Rate.

1 variance

Examine the relationship between power, sample size, and ratio for a 1 variance test. In Minitab, choose Stat > Power and Sample Size > 1 Variance.

2 variances

Examine the relationship between power, sample size, and ratio for a 2 variances test. In Minitab, choose Stat > Power and Sample Size > 2 Variances.

Equivalence tests

Use equivalence tests to determine whether the means for product measurements or process measurements are close enough to be considered equivalent.

1-sample equivalence test
Calculate one of the following values for a 1-sample equivalence test when you provide the other 2 values: power, required sample size, size of the difference. In Minitab, choose Stat > Power and Sample Size > Equivalence Tests > 1-Sample.
2-sample equivalence test
Calculate one of the following values for a 2-sample equivalence test when you provide the other 2 values: power, required sample size, size of the difference/ratio. In Minitab, choose Stat > Power and Sample Size > Equivalence Tests > 2-Sample.
Equivalence test with paired data
Calculate one of the following values for a 1-sample equivalence test when you provide the other 2 values: power, required sample size, size of the difference. In Minitab, choose Stat > Power and Sample Size > Equivalence Tests > Paired.
Equivalence test for 2x2 crossover design
Calculate one of the following values for a 1-sample equivalence test when you provide the other 2 values: power, required sample size, size of the difference. In Minitab, choose Stat > Power and Sample Size > Equivalence Tests > 2x2 Crossover Design.

Modeling statistics

Use to perform a power and sample size analysis on a one-way ANOVA or a DOE analysis.

One-way ANOVA

Examine the relationship between power, sample size, and maximum difference for one-way ANOVA. In Minitab, choose Stat > Power and Sample Size > One-Way ANOVA.

2-level factorial design
Examine the relationship between power and number of replicates for a 2-level factorial design. In Minitab, choose Stat > Power and Sample Size > 2-level factorial design.
Plackett-Burman design
Examine the relationship between power and number of replicates for a Plackett-Burman design. In Minitab, choose Stat > Power and Sample Size > Plackett-Burman design.
General full factorial design
Examine the relationship between power, number of replicates, and maximum difference for a general full factorial design. In Minitab, choose Stat > Power and Sample Size > General full factorial design.
By using this site you agree to the use of cookies for analytics and personalized content.  Read our policy