If a researcher were studying the effects of a teaching method on patient learning outcomes, how must he or she word the hypothesis to use a t-test to test for statistical differences? What type of data must he or she collect? Why?
I'll give you a few hints. You might try a correlated or dependent groups t-test on this situation. In a test of this type, each subject participates in two research conditions (a "before treatment" condition and an "after treatment" condition). In symbolic form, the null hypothesis would state the population means are equal and the alternate hypothesis would state the population means are not equal. If the null is rejected and the alternate accepted, you would then be able to conclude a statistical difference.
To use a t-test to test for statistical differences in the effects of a teaching method on patient learning outcomes, the researcher must formulate a hypothesis that specifically compares the means of two groups. The hypothesis should be phrased in a manner that suggests a difference or effect between the groups.
For example, the researcher might hypothesize that "The use of teaching method X will result in significantly higher patient learning outcomes compared to teaching method Y."
In this case, the researcher needs to collect numerical data on the patient learning outcomes for both groups. It is important to collect interval or ratio data, as these are the types of data that can be analyzed using a t-test. This would involve measuring the learning outcomes of patients who were subjected to teaching method X and comparing them to the learning outcomes of patients who received teaching method Y.
Collecting numerical data is necessary because a t-test relies on calculating the mean (average) of the data, which requires numerical values. Moreover, a t-test assumes that the data approximates a normal distribution, which means the data should be continuous and measured on an interval or ratio scale.
Furthermore, the researcher may also need to gather additional information about the patients, such as demographic or baseline characteristics, to ensure proper control of potential confounding variables.
By collecting and analyzing relevant numerical data, the researcher can then use a t-test to compare the means of the two groups and determine if there is a statistically significant difference in patient learning outcomes between the teaching methods being studied.