# How to do a two-sided hypothesis test for two sample means (in Julia)

See all solutions.

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?

## Solution

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 UnequalVarianceTTest().