- State the statistical
hypotheses. Population mean is represented by Greek letter μ (mu).
Null Hypothesis: The μ of the up river site equals the μ of the down river site Alternate Hypothesis: The μ of the up river site does not equal the μ of the down river site
*The null hypothesis states there is no difference between the population means and the alternate hypothesis states there is a difference between the population means.* - Decide whether to use a regular t-test or a paired t-test.
- Select the Alpha level.
- Decide whether to do a one-tailed t-test or a two-tailed t-test.
- Do the t-test using Excel or other software (Input all data points, not just the means).
- Interpret
the results and decide whether to:
**Statistical Hypothesis** Null H_{0}: μ Group #1 = μ Group #2 Alternate H_{A}:μ Group #1 ≠ μ Group #2Reject the Null Hypothesis and accept the Alternative Hypothesis This is the case when the absolute value of the t-statistic > t-critical; equivalently, p<alpha.or Retain the Null Hypothesis and reject the Alternate Hypothesis This is the case when the absolute value of the t-statistic < t-critical; equivalently, p>alpha)
In cases were the two groups being compared are not connected in any way, use the regular t-test. The case of the two groups of data collected at different sites on a river is an example where a regular t-test would be used. The data sets are independent of each other. Next, consider the case where a group of students takes a pre-test, is given some form of instruction and then takes a post-test to see if the instruction was effective. The two sets of test data are connected since the same students provided both sets; each student has a pre and a post test! In this example a paired t-test would be used. The alpha
level you choose determines the risk you are willing to
take of being wrong when you reject H Generally, researchers select two-tailed tests because they are not sure whether the difference between the means being compared will be positive or negative. In cases where the researcher has done some previous research and is sure the difference between the means will be either positive or negative, a one-tailed test can be used. However, there is a greater chance of being wrong if a one-tailed test is used so the researcher really needs to know a lot about the data he or she is studying!
The t-critical is a number that represents the size of the mean difference required for the alpha level selected. The t-statistic is a number that represents the actual size of the difference between the two test means. Let's assume α =0.05. If t-statistic> t-critical is true, it means that the difference between the two test means is so large it would only be found less than 5 times out of 100 in means coming from populations with the same distribution. Therefore, there is a 95% chance that the means come from different populations and the null hypothesis should be rejected. The p is the "calculated α." It represents the probability of the difference in the test means coming from the same population. Therefore, if p=0.04 (less than α), it means that the difference between the two test means is so large it would only be found 4 times out of 100 with means coming from populations with the same distribution and the null hypothesis should be rejected. Conversely, what if p=0.10 (greater than α)? In this case, the difference between the two test means would be found 10 times out of 100 with means coming from populations with the same distribution. ! Since α =0.05, the 10% chance is greater than the 5% chance allowed and the null hypothesis should be retained. Copyright © 1997 Central Virginia Governor's School for Science and Technology Lynchburg, VA |