Flashcards in Probability/Statistical Significance Deck (52)

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1

## What are the two ways studies can screw up?

###
1. caused by chance = random error

2. Not caused by chance = bias or systematic error

2

## What deals with random error in studies?

### Statistical inference

3

## If a study has a random error, is it likely to happen again if/when the study is repeated?

### NO

4

## An error that is inherent to the study method being used and results in a predictable and repeatable error for each observation is labeled a _____ error. What is it due to?

### Systematic error due to bias

5

## T/F: If you repeat a study that had a systematic error, it is likely to happen again

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TRUE

these errors are not caused by chance and there is no formal method to deal with them.

6

## What tests will estimate the likelihood that a study result was caused by chance?

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Tests of statistical inference

**a study result is called "statistically significant" if it is unlikely to be caused by chance

7

## Do if a study is statistically significant, is it clinically significant?

###
Not necessarily

Those terms have two different meanings

*even very small measures of association that are not large enough to matter can be statistically significant

8

## What is a chance occurrence?

### Something that happens unpredictably without discernible human intention or with no observable cause: caused by chance or random variation

9

## What is random variation?

### There is error in every measurement. If we measure something over and over again, we will get slightly different measurements each time AND a few measurements may be extreme

10

## What is statistical inference?

### Tells us: if we measure something only once, how sure are we that our measurement has been caused by chance

11

## What two methods are used for estimating how much random variation there is in our study and whether our result was likely to have been caused by chance?

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1. Confidence intervals

2. P-values

12

## _______ estimates how much random variation there is in our measurement

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Confidence intervals

-the range of values where the true value of our measurement could be found

13

## _____ are used to estimate whether the measure was likely to have been caused by chance or not

### P values

14

##
Will small sample sizes have a large 95% Confidence interval or small CI?

What about large sample sizes?

###
The larger the sample size, the smaller the confidence interval will be = more precise

*small samples have large CIs

*Large samples have small CIs

15

##
How do you interpret this statement?

"prevalence of disease was 8% (95% CI: 4%-12%)"

### The estimate of the prevalence from the study was 8%, but we are 95% confident that the true prevalence lies somewhere between 4% and 12%

16

## T/F: If the 95% CI for the odds ratio (OR) does NOT include one, the OR is statistically significant

###
TRUE

Ex: The odds ration was 3 (95% CI: 0.5 - 6)

**since this includes that the OR could have the value of ONE = it is NOT statistically significant

17

## How do you interpret 95% confidence intervals (95% CI) for odds ratios (OR)?

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1. OR greater than one, 95% CI does NOT include one : Positive association; statistically significant

2. OR greater than one, 95% CI includes one : NO association, NOT statistically significant

3. OR less than one, 95% CI does NOT include one : Negative association, statistically significant

4. OR less than one, 95% CI included one : No association, NOT statistically significant

18

## If the 95% CI for the relative risk (RR) does NOT include one, the RR (is / is not) statistically significant

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IS

*remember, when the RR = one, there is no association between the two test groups

19

## How do you interpret a RR greater than one, combined with a 95% CI that does NOT include one?

###
Positive association

Statistically significant

20

## How do you interpret a RR less than one, combined with a 95% CI that includes one?

###
No association

Not statistically significant

21

## How do you interpret a RR less than one, combined with a 95% CI that does NOT include one?

###
Negative association

Statistically significant

22

## T/F: P-value gives you information about the size of the test sample

###
FALSE

**it also does NOT give you any info about the range that you can expect to find the true value

23

## To be statistically significant, the p-value must be less than _____

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0.05

*if the p-value is greater than 0.06 - the association is NOT statistically significant and could have been caused by chance

24

## How do you interpret p-values that are less than 0.05?

###
We are 95% confident that an association as large as the one in our study was NOT caused by chance

or

We have 95% confidence that an association this large could not have been caused by chance

25

##
How do you interpret the following value?

OR or RR or PR = 3.0 (p = 0.02)

### Statistically significant. There is an association. We are 95% certain that an OR of 3.0 could NOT have been caused by chance.

26

## T/F: No matter how large the RR or OR; if the p-value is greater than 0.05, we must say there is no association

### TRUE

27

## How are p-values calculated?

###
Using statistical tests - tests for statistical inference:

1. Chi-squared test

2. Student's t test

3. Correlation

(need to know when/where to use these three tests - do not worry about calculations)

28

## When testing a hypothesis, can you prove something is true, untrue, or both?

###
Untrue

You cannot prove that something is true

You can't prove an association is true

But you can prove that either is NOT true --> Hence the use of a Null hypothesis

29

## What is a "Null" hypothesis?

###
hypothesis that suggests NO association

Used to be proven untrue and rejected - to confirm associations

30