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
