Week 11 - Inferential Statistics

Question 1

describe the purpose and distinguish between descriptive and inferential statistics

Answer 1

Descriptive statistics - statistics (mean) that describe data you have. Cannot generalise to a population with descriptive stats.

Inferential statistics - statistics (t-tests) that allow us to make inferences about a population beyond our data. Can generalise beyond sample to a population.

Question 2

explain the difference between estimation and hypothesis testing

Answer 2

Estimation - branch of inferential stats focused on obtaining estimates of the values of population parameters.
Point estimation- using sample data to estimate population values (mean= population average).
Interval estimation- researcher puts confidence intervals around point estimates.

Hypothesis testing - testing a predicted relationship (hypothesis) by making observations then comparing the observed facts with the hypothesis. Null hypothesis Ho and Alternative Hypothesis H1.

Question 3

define hypothesis testing and explain the steps in hypothesis testing

Answer 3

1 - state hypotheses
2 - set alpha level (usually 0.05)
3 - select statistical test
4 - conduct statistical test (obtain p-value)
5 - compare p-value to alpha level and use rule 1 or 2

Rule 1 - if p-value < alpha = reject Ho = finding is significant
Rule 2 - if p-value > alpha = fail to reject Ho = Finding is not significant.

Question 4

What are Type 1 and Type 2 errors

Answer 4

Type 1 - rejection of a true null hypothesis (false negative)
Type 2 - Failure to reject a false null hypothesis (false positive)