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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

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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

frequentists vs. bayesian example

100 coin flips all give 95 heads, what is the probability that the next flip will be a head?
freq.- 50%
bay.- 95%