Flashcards in BIO 330 Deck (379)

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361

## file-drawer problem

### studies that are not published- grad thesis, government research

362

## look for effects of study quality, Meta-analysis

###
do differences in n or methodology matter

-correlation btw n and ES?

-difference in observ. and exp. studies?

-base meta-analysis on higher quality studies

363

## pros of Meta-analysis

###
tells overall strength & variability of effect

can increase statistical power, reduce Type II error

can reveal publication bias

can reveal associations btw study type and study outcome

364

## cons/challenges of meta-analysis

###
assumes studies are directly comparable and unbiased samples

limited to accessible studies including necessary summary data

may have higher Type I error if publication bias is present

365

## what do we get out of the statistical process

###
a probability statement

this process is called Frequentist statistics, most commonly used

366

## What does frequentist statistics do

###
-answer probability statements if/given the null is true

-infer properties of a population using samples

-doesn't tell if null is true, not proof of anything

-useful, but must understand so not overinterpreted

367

## frequentists statistics developed

### Cohen, 1994; Null Hypothesis Sifnificance Testing

368

## why use frequentist statistics

###
appears to be objective and exact

readily available and easily used

everyone else uses it

scientists are taught to use it

supervisors & journals require it

369

## limits of frequentist statistics

###
–provides binary info only: significant or not

–does not provide means for assessing relative strength of support for alternate hypotheses

–failing to reject Ho does not mean Ho is true

–does not answer real question

370

## does not provide means for assessing relative strength of support for alternate hypotheses

### ex. conclude the slope of the line is not 0, how strong is the evidence that the slope is 0.4 vs 0.5

371

## real question

###
whether scientific hypothesis is true or false

-treatment has an effect (however small)

-if so, then Ho of no effect is false, but we are unable to show that Ho is false (or true)

-we can only show the probability of getting the data, if Ho is true

372

## question we CAN answer

### about the data, not the hypothesis- given the data, how likely is Ho to be true

373

## more limitations for frequentist stats

###
whether a result is significant depends on n, ES, alpha

significant does not always mean important

374

## larger n, ES, alpha

### increase likelihood of rejecting Ho- getting significant result

375

## significant does not necessarily mean important

### effects can be tiny and still statistically significant

376

## focus on p-values and Ho rejection

### distracts from the real goal- deciding whether data support scientific hypotheses and are practically/biologically important

377

## mostly we should be interested in

### size/strength/direction of an effect

378

## Bayesian statistics

### incorporate beliefs or knowledge of parameter values into analyses to contain population estimate

379