t-test for means with equal variances

In this tutorial we will discuss two sample t-test for testing difference between two population means when the population variances are unknown but equal.

Two sample t test with unknown and equal variances

Let $\overline{x}_1$ be the sample mean and $s_1$ be the sample standard deviation of a random sample of size $n_1$ from a population with mean $\mu_1$ and variance $\sigma^2_1$.

Let $\overline{x}_2$ be the sample mean and $s_2$ be the sample standard deviation of a random sample of size $n_2$ from a population with mean $\mu_2$ and variance $\sigma^2_2$.

Suppose the variances $\sigma^2_1$ and $\sigma^2_2$ are unknown but equal.

Assumptions

Assumptions for two sample $t$-test are as follows:

a. The population from which, the two samples drawn are Normal distributions.

b. The two population variances are unknown but equal.

We wish to test the hypothesis $H_0 : \mu_1 = \mu_2$.

The standard error of difference between means is

$$ SE(\overline{x}_1-\overline{x}_2) = s_p\sqrt{\frac{1}{n_1}+ \frac{1}{n_2}} $$

where $s_p$ is the pooled standard deviation and is given by

$$ s_p =\sqrt{\frac{(n_1-1)s_1^2 + (n_2-1)s_2^2}{n_1+n_2-2}}. $$

Step by Step Procedure

The step by step hypothesis testing procedure is as follows:

Step 1 State the hypothesis testing problem

The hypothesis testing problem can be structured in any one of the three situations as follows:

Situation Hypothesis Testing Problem
Situation A : $H_0: \mu_1=\mu_2$ against $H_a : \mu_1 < \mu_2$ (Left-tailed)
Situation B : $H_0: \mu_1=\mu_2$ against $H_a : \mu_1 > \mu_2$ (Right-tailed)
Situation C : $H_0: \mu_1=\mu_2$ against $H_a : \mu_1 \neq \mu_2$ (Two-tailed)

Step 2 Define the test statistic

The test statistic for testing above hypothesis is

$$ \begin{eqnarray*} t & =& \frac{(\overline{x}_1-\overline{x}_2)-(\mu_1-\mu_2)}{SE(\overline{x}_1-\overline{x}_2)}\\\\ & =& \frac{(\overline{x}_1-\overline{x}_2)-(\mu_1-\mu_2)}{s_p\sqrt{\frac{1}{n_1}+\frac{1}{n_2}}} \end{eqnarray*} $$

The test statistic $t$ follows Students' $t$ distribution with $n_1+n_2-2$ degrees of freedom.

Step 3 Specify the level of significance $\alpha$

Specify the value of level of significance $\alpha$.

Step 4 Determine the critical values

For the specified value of $\alpha$ determine the critical region depending upon the alternative hypothesis.

  • For left-tailed alternative hypothesis: Find the $t$-critical value using

$$ \begin{aligned} P(t < -t_\alpha) = \alpha. \end{aligned} $$

  • For right-tailed alternative hypothesis: $t_\alpha$.

$$ \begin{aligned} P(t > t_\alpha) = \alpha. \end{aligned} $$

  • For two-tailed alternative hypothesis: $t_{\alpha/2}$.

$$ P(t < -t_{\alpha/2} \text{ or } t > t_{\alpha/2}) = \alpha. $$

Step 5 Computation

Compute the test statistic under the null hypothesis $H_0$ using equation

$$ \begin{aligned} t_{obs} &= \frac{(\overline{x}_1-\overline{x}_2)-0}{s_p\sqrt{\frac{1}{n_1}+\frac{1}{n_2}}} \end{aligned} $$

Step 6 Decision (Traditional Approach)

It is based on the critical values.

  • For left-tailed alternative hypothesis: Reject $H_0$ if $t_{obs}\leq -t_\alpha$.
  • For right-tailed alternative hypothesis: Reject $H_0$ if $t_{obs}\geq t_\alpha$.
  • For two-tailed alternative hypothesis: Reject $H_0$ if $|t_{obs}|\geq t_{\alpha/2}$.

OR

Step 6 Decision ($p$-value Approach)

It is based on the $p$-value.

Alternative Hypothesis Type of Hypothesis $p$-value
$H_a: \mu_1<\mu_2$ Left-tailed $p$-value $= P(t\leq t_{obs})$
$H_a: \mu_1>\mu_2$ Right-tailed $p$-value $= P(t\geq t_{obs})$
$H_a: \mu_1\neq \mu_2$ Two-tailed $p$-value $= 2P(t\geq abs(t_{obs}))$

If $p$-value is less than $\alpha$, then reject the null hypothesis $H_0$ at $\alpha$ level of significance, otherwise fail to reject $H_0$ at $\alpha$ level of significance.

Endnote

In this tutorial, you learned the $t$-test for testing two population means with equal variances and the assumptions for $t$-test for testing two population means. You also learned about the step by step procedure to apply $t$-test for testing two population means with equal variances.

To learn more about other hypothesis testing problems, hypothesis testing calculators and step by step procedure, please refer to the following tutorials:

Let me know in the comments if you have any questions on $t$-test for two means with equal variances and your thought on this article.

VRCBuzz co-founder and passionate about making every day the greatest day of life. Raju is nerd at heart with a background in Statistics. Raju looks after overseeing day to day operations as well as focusing on strategic planning and growth of VRCBuzz products and services. Raju has more than 25 years of experience in Teaching fields. He gain energy by helping people to reach their goal and motivate to align to their passion. Raju holds a Ph.D. degree in Statistics. Raju loves to spend his leisure time on reading and implementing AI and machine learning concepts using statistical models.

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