# Probability and Statistical significance Flashcards

1
Q

What are 2 ways studies can screw up?

A

random error and bias/systematic error

2
Q

Does random error bias a study?

A

No. It may be wrong, but the study isn’t biased.

3
Q

How do you deal with random error?

A

Statistical Inference.

4
Q

T/F: a study who’s results are deemed statistically significant is also clinically relevant/meaningful

A

FALSE. You can have small measures of association be statistically significant, that are too small to matter.

5
Q

What are 2 values that we can use to estimate how much random variation there is in our study?

A

confidence intervals and P values.

6
Q

____ samples have ____ CIs, and _____ samples have ____ CIs

A. large samples, large CIs; small samples small CIs
B. small samples, large CIs, large samples, large CIs
C. small samples, large CIs, large samples, small CIs

A

C. small samples, large CIs, large samples, small CIs.

7
Q

What does the CI need to include or not in order for the odds ratio to be statistically significant?

A

The CI for the OR should not include 1, for the OR to be statistically significant.

Same rules apply to relative risk and prevalence ratios.

8
Q

What are P-values?

A

they essentially tell you the same thing as a CI, but CIs also tell you more. P-values only estimate whether a measured association was likely to have been caused by chance.

-no info about where true value is, or size of sample.

9
Q

What does the P-value need to be, for it to be statistically significant?

A

It needs to be less than 0.05

10
Q

T/F: you can prove that something is true.

A

False. You can only prove that something is not true.

11
Q

What is the null hypothesis?

A

The hypothesis of no association…there is no association between exposure and dz

12
Q

What is the alternative hypothesis?

A

That there is an association between exposure and dz.

13
Q

If your P-value is less than 0.05, do you accept or reject the null hypothesis?

A

reject it. There is an association, so you accept the alternative hypothesis.

14
Q

What is type I error?

A

False positive. You reject the null hypothesis when it is not false (so you are saying there is an association when there isn’t)

15
Q

What is a Type II error?

A

False negative. You are failing to reject the null hypothesis, when it is actually false.

16
Q

Which of the following is very sensitive to extreme values?

A. median
B. Mean
C. mode

A

B. Mean

17
Q

What is dispersion?

A

Measures how closely the values are gathered around the center of distribution

18
Q

What are 2 measures of dispersion?

A

range and standard deviation

19
Q

When are Chi-squared tests used?

A

For categorical data. They measure difference in proportions.

20
Q

When are student’s t-test used?

A

when looking at continuous data. Looks at the difference in means

21
Q

If your independent (explanatory) variable is categorical, and our outcome variable is categorical, what test should you use?

A

Chi-squared

22
Q

If your independent (explanatory) variable is categorical, and our outcome variable is continuous, what test should you use?

A

Student’s t-test.

23
Q

If your independent (explanatory) variable is continuous, and our outcome variable is continuous, what test should you use?

A

correlation

24
Q

What does the correlation coefficient tell us?

A

it indicaes the strength and direction of a linear relationship between 2 continuous relationships. (strong = >.80)