Imagine that you conducted a hypothesis test examining how automobile ownership (i.e., someone owns an automobile or does not own an automobile) affects happiness (measured by a survey). You found that people who do not own automobiles tend to be significantly happier than those that do own an automobile. You then computed the effect size, d = 0.75. a) Why is it important to compute an effect size? b) Please interpret the Cohen’s d value (d = 0.75) by explaining what it means in terms of automobile ownership and happiness.

What is the consequence of a Type I error?

What proportion of a normal distribution is located between the mean and z = 1.40?

For a normal population with a mean of 80 and a standard deviation of 10, what is the probability of obtaining a sample mean greater than M = 75 for a sample of n = 25 scores?

Which of the following accurately describes the effect of increasing the sample size?

Which combination of factors will increase the chances of rejecting the null hypothesis?

If the alpha level increases:

A sample of n = 16 scores is selected from a population with µ = 80 with σ = 20.  On average, how much error would be expected between the sample mean and the population mean?

Which of the following accurately describes a hypothesis test?

What proportion of a normal distribution is located in the tail beyond z = 2.14?