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Flashcards in Precision and Statistical Issues Deck (22)
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1

What are two components of Accuracy

1. Validity
2. Precision

2

What is Accuracy

The degree to which a measurement represents the true value of what is being measured.

3

Validity

타당성
the degree to which the results and conclusions of a study reflect the true state of nature.

Also the degree to which the study is free of systematic error (is unbiased)

4

Precision

정확/신중

the degree to which random error affects the parameter estimates within a study

The degree to which statistical estimates would be reproducible with repeated sampling

5

two types of validity

1. internal validity
2. external validity

6

internal validity

the degree to which a study is free of bias or systematic error

internal validity is prerequisite of external validity

7

external validity

the extent to which the results of a study may be applied to populations or groups that were not subjects of the study

8

bias

systematic error in study design or conduct that leads to results that deviate from the truth

9

If study is unbiased...

repeatedly conducting that study will produce correct results on verage

10

potential sources of bias

systematic measurement error (as distinguished from random sampling error)

flaws in study conception, design or analysis

conscious or unconscious selection in obtaining or interpreting results

11

measure of precision

reciprocal of the VARIANCE of a measure or estimate

12

increasing the precision of an estimate is equivalent to

reducing its variance

13

measure of imprecision

standard error: series of repeated estimates of a single quantity

14

larger standard deviation/error/variance means

less precision in your estimates

15

measure of both precision and imprecision

confidence interval: looking at the CI gives an idea of how precise or imprecise the measures are.

16

testing for the presence of effects

point estimates
significance test (Fisher)
Hypothesis test (Neyman-pearson)
p-cause
Bayesian analyses

17

measuring effect sizes

point estimates and CI
p-value functions (= ci functions)
regression modeling
Bayesian analyses

18

H0

there is no effect (lack of difference), so if outcome is positive, reject H0

19

p-value

probability of obtaining a value of the test statistic for that association equal to, or more extreme than, the value actually observed, if H0 is true and if in fact the statistical model used to derive the test statistic is valid and no bias is present

20

p-value is sensitive to sample size

very small effect in very large sample can be statistically significant even they don't mean anything

21

p-value is affected by precision

but provides no or very little information about precision

22

p-value nd effect size and sample size

p-value mixes information about effect size and sample size; it is difficult to tell which is more important in a given case