Exam 2 - Handout 6a - Inferential Stats Practice Questions

Question 1

Which of the following best describes the central limit theorem?

A. The mean of sample means is always equal to the population mean

B. As sample size increases, the distribution of sample means approaches a normal distribution

C. The standard deviation of sample means is always equal to the population standard deviation

D. The distribution of sample means is always skewed for small sample sizes

Answer 1

B. As sample size increases, the distribution of sample means approaches a normal distribution

Question 2

What is the primary purpose of inferential stats?

Answer 2

To apply findings from a sample to a target population

Question 3

How does the t-distribution differ from the z-distribution, and when is it typically used?

Answer 3

The t-distribution has thicker tails than the z-distribution and is used when the population standard deviation is unknown

Question 4

What is the primary difference between statistical significance and clinical significance?

Answer 4

Statistical significance refers to the results of a statistical analysis

Clinical significance refers to the practical importance of findings

Question 5

How does the bayesian approach to statistical inference differ from the frequentist approach?

Answer 5

It incorporates prior knowledge and updates it w/ new evidence

Question 6

What are key components of a confidence interval calculation, and how is it interpreted?

Answer 6

Calculated as: point estimate +/- (critical value)(standard error of the estimate)

It is interpreted as a range that likely contains the true population parameter w/ a certain level of confidence

Question 7

What is the difference between point estimation and interval estimation in statistical analysis?

Answer 7

Point estimation provides a single value for a population parameter

While interval estimation constructs a confidence interval to estimate a range of possible values for the parameter

Question 8

In hypothesis testing, what does a Type II error represent?

Answer 8

Failing to reject the null hypothesis when it is false

Question 9

Explain the concept of power in hypothesis testing and its relationship of Type II error?

Answer 9

Power is the probability of rejecting the null hypothesis when it is false, and is equal to 1 - the probability of Type II error

Question 10

How does the binomial distribution describe binary outcomes? What is the relationship between its parameters?

Answer 10

The binomial distribution describes outcomes w/ two possible results (eg. success/failure)

The probability of success (p) and failure (q) sum to 1