comparing means Flashcards
(11 cards)
What is the purpose of comparing means in healthcare research?
To determine if differences between groups are statistically significant, e.g., comparing treatment vs control, or determining if group values deviate from a norm.
When is the mean a useful estimate of a population value?
When data is on a numerical scale, normally distributed, and has homogeneity of variance.
What are examples of questions for comparing mean scores?
Is the group’s mean significantly different from a set value? Are two groups significantly different? Do values differ under two conditions (e.g., pre vs post treatment)?
What are the three types of t-tests?
Single sample t-test, Independent samples t-test, Paired samples t-test.
What are the assumptions for using a t-test?
Data must be numerical (interval or ratio), normally distributed, and have equal variance (homogeneity of variance).
What is the null hypothesis in a t-test?
That there is no significant difference between the group means being compared.
What outputs does a t-test provide?
A test value (t), a p-value, and a decision on whether to reject the null hypothesis.
What does the p-value tell us?
The probability that the observed effect occurred by chance under the null hypothesis. Lower p-values suggest stronger evidence against the null.
Give an example of a research question for an independent t-test.
Do younger adults (18–25) differ in test scores from older adults (65–80)?
What would be the best test for comparing pre- and post-treatment scores from the same group?
A paired samples t-test.
What does a non-significant t-test result imply?
That there is no statistically significant difference between the compared means.
