There is always some likelihood that the changes you observe in your participants’ knowledge, attitudes, and behaviors are due to chance rather than to the program. If the power is less than 0.8, you will need to increase your sample size. When these values are entered, a power value between 0 and 1 will be generated. The sample size you are planning to use.The expected effect size (See the last section of this page for more information.),.See the next section of this page for more information.), The alpha value or significance level you are using (usually 0.01 or 0.05.See Step 6 if you are not familiar with these tests.), What type of test you plan to use (e.g., independent t-test, paired t-test, ANOVA, regression, etc.Several software programs are available for free on the Internet and are described below.įor any power calculation, you will need to know: Unfortunately, these calculations are not easy to do by hand, so unless you are a statistics whiz, you will want the help of a software program. To ensure that your sample size is big enough, you will need to conduct a power analysis calculation. This should intuitively make sense as a larger sample means that you have collected more information - which makes it easier to correctly reject the null hypothesis when you should. Generally speaking, as your sample size increases, so does the power of your test. How do I use power calculations to determine my sample size? 8 or greater that is, you should have an 80% or greater chance of finding a statistically significant difference when there is one. It is generally accepted that power should be. In other words, power is the probability that you will reject the null hypothesis when you should (and thus avoid a Type II error). Power refers to the probability that your test will find a statistically significant difference when such a difference actually exists. This type of mistake is called a Type II error. Likewise, it is possible that when a difference does exist, the test will not be able to identify it. With any statistical test, however, there is always the possibility that you will find a difference between groups when one does not actually exist. Statistical tests look for evidence that you can reject the null hypothesis and conclude that your program had an effect. Using the example above, the alternative hypothesis is that students’ post-trip level of concern for the environment will differ from their pre-trip level of concern. The alternative hypothesis – This hypothesis predicts that you will find a difference between groups.For example, if you are measuring students’ level of concern for the environment before and after a field trip, the null hypothesis is that their level of concern will remain the same. The null hypothesis – This hypothesis predicts that your program will not have an effect on your variable of interest.When you conduct an inferential statistical test, you are often comparing two hypotheses: To understand power, it is helpful to review what inferential statistics test.
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