If we have two samples, $x_1,\ldots,x_n$ and $x’_1,\ldots,x’_m$, and we compute the mean of each one, we might want to ask whether the two means seem approximately equal. Or more precisely, is their difference statistically significant at a given level?
- How to compute a confidence interval for a population mean
- How to do a two-sided hypothesis test for a sample mean
- How to do a one-way analysis of variance (ANOVA)
- How to do a one-sided hypothesis test for two sample means
- How to do a hypothesis test for a mean difference (matched pairs)
- How to do a hypothesis test for a population proportion
If we call the mean of the first sample $\bar x_1$ and the mean of the second sample $\bar x_2$, then this is a two-sided test with the null hypothesis $H_0:\bar x_1=\bar x_2$. We choose a value $0\leq\alpha\leq1$ as the probability of a Type I error (false positive, finding we should reject $H_0$ when it’s actually true).
1 2 3 4 5 6 7 8 9 10 11 # Replace these first three lines with the values from your situation. alpha = 0.10 sample1 = [ 6, 9, 7, 10, 10, 9 ] sample2 = [ 12, 14, 10, 17, 9 ] # Run a one-sample t-test and print out alpha, the p value, # and whether the comparison says to reject the null hypothesis. using HypothesisTests p_value = pvalue( UnequalVarianceTTest( sample1, sample2 ) ) reject_H0 = p_value < alpha alpha, p_value, reject_H0
1 (0.1, 0.050972837418476996, true)
In this case, the $p$-value was less than $\alpha$, so the sample gives us enough evidence to reject the null hypothesis at the $\alpha=0.10$ level. The data suggest that $\bar x_1\neq\bar x_2$.
When you are using the most common value for $\alpha$, which is $0.05$ for the $95\%$ confidence interval, you can simply print out the test itself and get a detailed printout with all the information you need, thus saving a few lines of code. Note that this gives a different answer below than the one above, because above we chose to use $\alpha=0.10$, but the default below is $\alpha=0.05$.
1 UnequalVarianceTTest( sample1, sample2 )
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 Two sample t-test (unequal variance) ------------------------------------ Population details: parameter of interest: Mean difference value under h_0: 0 point estimate: -3.9 95% confidence interval: (-7.823, 0.02309) Test summary: outcome with 95% confidence: fail to reject h_0 two-sided p-value: 0.0510 Details: number of observations: [6,5] t-statistic: -2.4616581720814326 degrees of freedom: 5.720083530052662 empirical standard error: 1.584297951775486
Here we did not assume that the two samples had equal variance.
If in your case they do, you can use
EqualVarianceTTest() instead of
Content last modified on 24 July 2023.
Contributed by Nathan Carter (email@example.com)