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331

Fisher's exact test assumptions

random samples

332

Fisher's Ho

state of A and B are independent

333

conduct Fisher's

–list all possible 2x2 tables w/ results as or more extreme than observed table
–p-value is sum of the Pr of all extreme tables under Ho of independence
–assess null

334

Computer-Intensive methods

cheap speed
hypothesis testing- simulation, permutation (randomization)
standard errors, CI- bootstrapping

335

hypothesis testing, simulation

–simulates sampling process many times- generate null distribution from simulated data
–creates a 'population' w/ parameter values specified by Ho
–used commonly when null distr. unknown

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simulation to generate null distribution

1.create and sample imaginary population w/ parameter values as specified by Ho
2.calculate test statistic on simulated sample
3.repeat 1&2 large number of times
4.gather all simulated test statistic values to form null distr.
5.compare test statistic from data to null distr. to approx. p-value and assess Ho

337

generated null distribution

P-value ~ fraction of simulated X^2 values ≥ observed X^2
none ≥ observed, P < 0.0001

338

Permutation tests (Randomization test)

test hypotheses of association between 2 variables; randomization done w/o replacement; needs 'parameter' for association btw 2 variables

339

Permutation test used when

assumption of other methods are not met or null distribution is unknown

340

Permutation steps

1.Create permuted data set w/ response variable randomly shuffled w/o replacement
2.calculate measure of association for permuted sample
3. repeat 1&2 large number of times
4. Gather all permuted values of test statistic to form null distribution
5. Determine approximate P-value and assess Ho

341

Bootstrapping

calculate SE or CI for parameter estimate
useful if no formula or if distribution unknown
randomly 'resamples' from the data with replacement to estimate SE or CI
ex. median

342

bootstrapping steps

1.random sample w/ replacement- 1st bootstrap sample
2.calculate estimate using bootstrap sample
3.repeat many times
4.calculate bootstrap SE
*only sampling from original sample values

343

simulation

mimics repeated sampling under Ho

344

permutation

randomly reassigns observed values for one of two variables

345

bootstrapping

used to calculate SE by resampling from the data set

346

Jack-knifing

leave-one-out method for calculating SE

347

Jack-knifing

gives same result every time (unlike boot strapping)
calculates mean from n-1, then n-2, then n-3

348

statistical significance

observed difference (effect) are not likely due to random chance

349

practical significance

is the difference (effect) large enough to be important or of value in a practical sense

350

effect size

ES– degree or strength of effect
ex. magnitude of relationship btw 2 variables
3 ways to quantify

351

3 ways to quantify ES

standardized mean difference
correlation
odds-ratio

352

standardized ean difference

Cohen's d

353

can find statistical significance

with a large n, which may not be large effect size, and may not be significant at lower n

354

Quantifying ES

2% difference btw population and sample means
difficult to interpret mean differences w/o accounting for variance (s^2)
Cohen standardized ES w/ variance

355

Cohen's d

simplest measure of ES
difference btw means / Sp
standardizes, puts all results on same scale (makes meta-analysis possible)

356

Meta-analysis

analysis of analysis
synthesis of multiple studies on a topic that gives an overall conclusion; increases sig. of individual studies (larger n)
black line = 1-1 line - no difference, no more, no less

357

steps in meta-anlysis

define question to create one large study- general or specific; review literature to collect all studies- exhaustively; compute effect sizes and mean ES across al studies; look for effects of study quality

358

literature search

beware of 'garbage in, garbage out', publication bias, file-drawer problem

359

publication bias

bias- studies that weren't published- lower n, insignificant, low effect

360

garbage in, garbage out

justify why studies are not included, what is considered poor science?