Biostats 2- Inferential Statistics

Question 1

This is the concept when there is variation in the values of a sample when compated to other samples or to the population, usually just by unfortunate random chance.

Answer 1

Random sampling variation

Question 2

What are the 2 methods for investigating the role of random sampling variation in a study’s results?

Hint: 1 is the range for a level of precision, the other is the testing of significance & p-values.

Answer 2

Confidence Intervals

Hypothesis testing

Question 3

This is the transformative statment based on the clinical question of interest (research), or a claim or statement about a property of a population (stats).

Answer 3

Hypothesis

Question 4

This is the type of hypothesis where there is no difference, and the calculated difference is due to chance.

Answer 4

Null Hypothesis (Ho)

Question 5

If we end up FAILING to reject Ho, what does that mean?

Answer 5

That the difference is due to random chance alone (random sampling variation)

(like if the Ho assumes a conclusion of no therapy effect of a drug, and we reject Ho, we are saying that there is no difference in the effectiveness in the drugs therapy and the drug sucks balls)

Question 6

This is the type of hypothesis where a real difference exists, and the difference is not due to chance.

Answer 6

Alternative hypothesis (Ha)

Question 7

If we end up accepting Ha, what does that mean?

Answer 7

It’s basically the same thing as rejecting Ho, where there is a difference in the hypothesis.

Question 8

What is the test for the null hypothesis?

Answer 8

Test of significance

Question 9

This is when there is statistical evidence that a real, meaningful difference exists (not due to random sampling variation).

Answer 9

Statistically significant difference

Question 10

This is the type of significance test used when the dependent variable is continuous AND data is normally distributed.

Answer 10

Parametric tests

Question 11

This is the type of significance test used when the dependent variable is categorical OR continuous but fails the normality assumption.

Answer 11

Nonparametric tests

Question 12

What is the test used to measure the temp of each great lake?

Answer 12

1-way ANOVA

Question 13

This is the probability that the difference is due to chance (random sampling variation).

Answer 13

P-value

Question 14

Do higher or lower P-values show the more likely the differenece id eo to chance (therefore supporting Ho).

Answer 14

High P-values –> support Ho

Question 15

What is the usual value for the cutoff for P-values (alpha)?

Answer 15

0.05

Question 16

What happens to alpha if you use a 2-tailed test?

Answer 16

You gotta split it in 1/2 for each tail so each tail now has an alpha = 0.025

Question 17

What is necessary for clinical significance, for the decision to modify treatment?

Answer 17

Statistical significance

Question 18

What does the absolute risk include that the relative risk doesnt include?

Answer 18

Context/prevalence

Question 19

This si the type of error where u mistakenly reject the Ho when it is actually true (false +).

Answer 19

Type I (alpha) error

Question 20

What should you try to do to alpha to limit type I errors?

Answer 20

Lower it (usually to 5%)

Question 21

What happens in type II (beta) errors to give u a false negative?

Answer 21

You accept the Ho when its actually FALSE

Question 22

What is the normally beta values?

Answer 22

0.20

Question 23

What is the eqn for Power?

Answer 23

Heavy weights + muscle milk = Power

Question 24

What is the eqn for statistical Power?

Answer 24

Power = 1- beta

Question 25

What is the purpose of statistical power?

Answer 25

To avoid false negatives (errors) and finding true negatives when one actually exists.

Question 26

What is the % goal for adquate power?

Answer 26

0.80

Question 27

What are the 3 ways of increasing statistical power and thereby avoiding false negatives?

Answer 27

1. ↓ alpha
2. ↓ variation (↑ sample size and good data collection)
3. ↑ effect size (magnitude of a relationship or difference between 2 mean values)