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.
μ₁: population mean of Rating when Hospital = A |
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µ₂: population mean of Rating when Hospital = B |
Difference: μ₁ - µ₂ |
Hospital | N | Mean | StDev | Variance | Minimum | Median | Maximum |
---|---|---|---|---|---|---|---|
A | 20 | 80.30 | 8.18 | 66.96 | 62.00 | 79.00 | 98.00 |
B | 20 | 59.30 | 12.43 | 154.54 | 35.00 | 58.50 | 89.00 |
Mean of A - Mean of B = 21.000 |
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Null hypothesis | H₀: μ₁ - µ₂ = 0 |
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Alternative hypothesis | H₁: μ₁ - µ₂ ≠ 0 |
Number of Resamples | Average | StDev | P-Value |
---|---|---|---|
1000 | -0.185 | 4.728 | < 0.002 |
In these results, the null hypothesis states that the difference in the mean rating between two hospitals is 0. Because the p-value is less than 0.002, which is less than the significance level of 0.05, the decision is to reject the null hypothesis and conclude that the ratings of the hospitals are different.