A histogram divides sample values into many intervals and represents the frequency of data values in each interval with a bar.
The histogram visually shows the results of the hypothesis test. Minitab adjusts the data so that the center of the resamples is the same as the hypothesized mean. For a onesided test, a reference line is drawn at the mean of the original sample. For a twosided test, a reference line is drawn at the mean of the original sample and at the same distance on the opposite side of the hypothesized mean. The pvalue is the proportion of sample means that are more extreme than the values at the reference lines. In other words, the pvalue is the proportion of sample means that are as extreme as your original sample when you assume that the null hypothesis is true. These means are colored red on the histogram.
An individual value plot displays the individual values in the sample. Each circle represents one observation. An individual value plot is especially useful when you have relatively few observations and when you also need to assess the effect of each observation.
Minitab displays an individual value plot only when you take only one resample. Minitab displays both the original data and the resample data.
Minitab adjusts the data so that the center of the resamples is the same as the hypothesized mean. First, Minitab calculates the difference between the hypothesized mean and the mean of the original sample. Then Minitab adds or subtracts the difference to each value in the original sample. Resamples are taken from this adjusted data.
In the output, the null and alternative hypotheses help you to verify that you entered the correct value for the hypothesized mean.
In these results, the null hypothesis is that the population mean is equal to 12. The alternative hypothesis is that the mean is less than 12.
 
 

The number of resamples is the number of times Minitab takes a random sample with replacement from your original data set. Usually, a large number of resamples works best.
Minitab adjusts the data so that the center of the resamples is the same as the hypothesized mean. First, Minitab calculates the difference between the hypothesized mean and the mean of the original sample. Then Minitab adds or subtracts the difference to each value in the original sample. Resamples are taken from this adjusted data. The sample size for each resample is equal to the sample size of the original data set. The number of resamples equals the number of observations on the histogram.
The mean is the sum of all the means in the bootstrapping sample divided by the number of resamples. Minitab adjusts the data so that the center of the resamples is the same as the hypothesized mean.
Minitab displays two different mean values, the mean of the observed sample and the mean of the bootstrap distribution. The mean of the observed sample is an estimate of the population mean. The mean of the bootstrap distribution is usually close to the hypothesized mean. The larger the difference between these two values, the more evidence you would expect against the null hypothesis.
The standard deviation is the most common measure of dispersion, or how spread out the data are about the mean. The symbol σ (sigma) is often used to represent the standard deviation of a population, while s is used to represent the standard deviation of a sample. Variation that is random or natural to a process is often referred to as noise. Because the standard deviation is in the same units as the data, it is usually easier to interpret than the variance.
The standard deviation of the bootstrap samples (also known as the bootstrap standard error) is an estimate of the standard deviation of the sampling distribution of the mean. Because the bootstrap standard error is the variation of sample means, whereas the standard deviation of the observed samples is the variation of individual observations, the bootstrap standard error is smaller.
Use the standard deviation to determine how spread out the means from the bootstrap sample are from the overall mean. A higher standard deviation value indicates greater spread in the means. A good rule of thumb for a normal distribution is that approximately 68% of the values fall within one standard deviation of the overall mean, 95% of the values fall within two standard deviations, and 99.7% of the values fall within three standard deviations.
Use the standard deviation of the bootstrap samples to estimate the precision of the bootstrap means. A smaller value indicates more precision. A larger standard deviation in the original sample usually results in a larger bootstrap standard error and a less powerful hypothesis test. A smaller sample size also usually results in a larger bootstrap standard error and a less powerful hypothesis test.
The pvalue is the proportion of sample means that are as extreme as your original sample when you assume that the null hypothesis is true. A smaller pvalue provides stronger evidence against the null hypothesis.
Use the pvalue to determine whether the population mean is statistically different from the hypothesized mean.