Sure, here are the steps involved in stating the null and alternative hypotheses, identifying the statistical test used to determine statistical significance, identifying the test statistic, noting it, and explaining its meaning, identifying the significance level used in each study, identifying whether the authors found support for their hypotheses, considering sample size and Type I and Type II error, and explaining the implications of each finding:
1. State the null and alternative hypotheses
The null hypothesis is the hypothesis that there is no difference between the two groups being compared. The alternative hypothesis is the hypothesis that there is a difference between the two groups being compared.
2. Identify the statistical test used to determine statistical significance
The statistical test used to determine statistical significance will depend on the type of data being analyzed and the number of groups being compared. Some common statistical tests include the t-test, analysis of variance (ANOVA), and chi-squared test.
3. Identify the test statistic, note it, and explain its meaning
The test statistic is a number that is calculated from the data and used to determine whether the null hypothesis should be rejected. The meaning of the test statistic will vary depending on the type of statistical test being used.
4. Identify the significance level used in each study
The significance level is the probability of making a Type I error. A Type I error is the error of rejecting the null hypothesis when it is actually true. The significance level is typically set at 0.05 or 0.01.
5. Identify whether the authors found support for their hypotheses
The authors found support for their hypotheses if the test statistic was statistically significant at the chosen significance level. A statistically significant test statistic means that there is a small probability that the difference between the two groups being compared could have occurred by chance.
6. Consider sample size and Type I and Type II error
The sample size is the number of observations in each group being compared. The sample size affects the power of the statistical test. The power of a statistical test is the probability of correctly rejecting the null hypothesis when it is actually false.
7. Explain the implications of each finding
The implications of each finding will depend on the specific research question being asked. However, in general, a statistically significant finding means that there is a difference between the two groups being compared. This difference may be due to a number of factors, such as the independent variable being tested.
Here is an example of how to apply these steps to a real-world research study:
A study was conducted to test the effectiveness of a new drug for treating depression. The study compared two groups of people: one group received the new drug and the other group received a placebo. The results of the study showed that the group that received the new drug had a significantly lower rate of depression than the group that received the placebo.
The null hypothesis for this study would be that there is no difference in the rate of depression between the two groups. The alternative hypothesis would be that the rate of depression is lower in the group that received the new drug.
The statistical test used to determine statistical significance in this study was a t-test. The test statistic for this study was 2.57. The significance level for this study was 0.05.
Because the test statistic was statistically significant at the chosen significance level, the authors found support for their hypothesis that the new drug is effective for treating depression.
The sample size for this study was 100 participants in each group. The power of the statistical test for this study was 0.80.
The implications of this finding are that the new drug is an effective treatment for depression. This finding has important implications for the treatment of depression and could lead to new guidelines for the treatment of this disorder.
