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 means from each resample. The bootstrap distribution should appear to be normal. If the bootstrap distribution is non-normal, you cannot trust the results.
50 resamples
1000 resamples
The distribution is usually easier to determine with more resamples. For example, in these data, the distribution is ambiguous for 50 resamples. With 1000 resamples, the shape looks approximately normal.
In this histogram, the bootstrap distribution appears to be normal.
Step 2: Determine whether the test results are statistically significant
To determine whether the difference between the population mean and the hypothesized mean is statistically significant, compare the p-value to the significance level. 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 means is statistically significant (Reject H0)
If the p-value is less than or equal to the significance level, the decision is to reject the null hypothesis. You can conclude that the difference between the population mean and the hypothesized mean is statistically significant. To calculate a confidence interval and determine whether the difference is practically significant, use Bootstrapping
for 1-sample function. For more information, go to Statistical and practical significance.
P-value > α: The difference between the means is not statistically significant (Fail to reject H0)
If the p-value is greater than the significance level, the decision is to fail to reject the null hypothesis. You do not have enough evidence to conclude that the difference between the population mean and the hypothesized mean is statistically significant.
Observed Sample
Variable
N
Mean
StDev
Variance
Sum
Minimum
Median
Maximum
Time
16
11.331
3.115
9.702
181.300
7.700
10.050
16.000
Randomization Test
Null hypothesis
H₀: μ = 12
Alternative hypothesis
H₁: μ < 12
Number of Resamples
Mean
StDev
P-Value
1000
11.9783
0.7625
0.199
Key Results: P-Value
In these results, the alternative hypothesis states that the mean reaction time is less than 12 minutes. Because the p-value is 0.203, which is greater than the significance level of 0.05, the decision is to fail to reject the null hypothesis. You cannot conclude that the mean reaction time is less than 12 minutes.
