Complete the following steps to interpret a 1-sample function bootstrapping analysis. Key output includes the histogram, the estimate of the parameter, and the confidence interval.

Step 1: Examine the shape of your bootstrap distribution

Use the histogram to examine the shape of your bootstrap distribution. The bootstrap distribution is the distribution of the chosen statistic from each resample. The bootstrap distribution should appear to be normal. If the bootstrap distribution is non-normal, you cannot trust the bootstrap results.

Step 2: Determine a confidence interval for the population parameter

First, consider the statistic from the bootstrap sample, and then examine the confidence interval.

The statistic of the bootstrap sample is an estimate of the population parameter. Because the statistic is based on sample data and not the entire population, it is unlikely that the sample statistic equals the population parameter. To better estimate the population parameter, use the confidence interval.

Confidence intervals are based on the sampling distribution of a statistic. If a statistic has no bias as an estimator of a parameter, its sampling distribution is centered at the true value of the parameter. A bootstrapping distribution approximates the sampling distribution of the statistic. Therefore, the middle 95% of values from the bootstrapping distribution provide a 95% confidence interval for the parameter. The confidence interval helps you assess the practical significance of your estimate for the population parameter. Use your specialized knowledge to determine whether the confidence interval includes values that have practical significance for your situation.

Note

Minitab does not calculate the confidence interval when the number of resamples is too small to obtain an accurate confidence interval.

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