Usually, a significance level (denoted as α or alpha) of 0.05 works well. A significance level of 0.05 indicates a 5% risk of concluding that a difference exists when there is no actual difference. P-value ≤ α: The difference between the proportions is statistically significant (Reject H 0)

Complete the following steps to interpret a runs test. Key output includes the observed number of runs, the expected number of runs, and the p-value.

An editor of a video game web site wants to determine whether the proportion of readers that own a PlayStation console is greater than 0.5. Choose Calc > Resampling > Randomization Test for 1-Sample Proportion .

The test for number or runs up and down is based on the total number of observed runs up or down. A run up is an upward run of consecutive points that exclusively increases. A run down is a downward run of consecutive points that exclusively decreases.

In these results, based on a sample size of 5 in each of the 4 groups, and a maximum difference of 4, Minitab calculates that the power of the test to detect a difference between the smallest mean and the largest mean is approximately 0.83.

For example, if you perform a one-way ANOVA using the default hypotheses, an α of 0.05 indicates a 5% risk of concluding that a difference exists when a difference does not actually exist.

According to the AIAG, you need at least 5 distinct categories to have an adequate measuring system. For more information, go to Using the number of distinct categories in a gage R&R study . The graphs also provide the following information about the measurement system:

The number of distinct categories represents the number of non-overlapping confidence intervals that span the range of product variation. The number of distinct categories also represents the number of groups within your process data that your measurement system can discern.

Minitab uses a normal approximation method to calculate the probability of getting an equal or greater number of runs. The probability is quite good if at least 10 observations exist both above and below K. The normal approximation for runs test is given by:

Complete the following steps to interpret Power and Sample Size for 2-Level Factorial Design. Key output includes the effect, the number of replicates, the power, the number of center points, the total number of runs, and the power curve.