Comparing Means 1:

• Why is comparing means important in experiments?

  It allows us to see if a change in an independent variable (IV) leads to a change in a dependent variable (DV).
• What is the question posed regarding the average height of men and women?

  Do men and women have the same average (mean) height?
• What is the goal when comparing the means of two groups?

  To find out if the mean of one group is equal to the mean of another group.
• What is the independent variable in the height comparison study?

  Sex (male and female).
• What is the dependent variable in the height comparison study?

  Height (cm).
• What is the null hypothesis in the height comparison study?

  x̄1 = x̄2
• What is the alternative hypothesis in the height comparison study?

  x̄1 ≠ x̄2
• What is the purpose of measuring many men and women in the height study?

  To see if their data is from the same distribution or different distributions.
• What issue arises when comparing means from two samples?

  The two means could come from the same population.
• What is the challenge in studying populations versus samples?

  We want to know about the population but can only measure a sample.
• What do descriptive statistics provide?

  Numbers describing the sample.
• What do inferential statistics allow researchers to do?

  Use descriptive stats to find out about the population.
• What is a problem with data from two samples?

  Data from two samples will give different means, even if they come from the same population.
• What does it mean if there is a difference between the samples but no difference in the population?

  It raises the question of probability regarding the observed difference.
• What does the p-value indicate?

  How well the data from the sample fits the null hypothesis.
• What does the p-value represent in the context of comparing means?

  The probability of seeing the difference in the means in the sample given there was no difference in the means in the population.
• What does a low p-value indicate?

  There is a real difference in the population.
• What is the threshold for statistical significance in this context?

  p < 0.05 (less than 5%).
• What does "non-significant" mean in statistical terms?

  It is NOT the same as "insignificant".
• What does a t-test help to determine?

  How many standard errors the means are away from each other.
• What does the p-value in a t-test indicate?

  How confident we can be that the sample means came from different distributions.
• Where can one find the demonstration for conducting a t-test in SPSS?

  In the lecture slides titled "Comparing means part 1 - lecture slides (2).pptx".