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sampling distribution
a distribution of statistics obtained by selecting all possible samples of a specific size from a population

the power of a test
 the probability that the test will correctly reject a false null hypothesis
 It will detect a treatment effect if one exists
 Power=1–β or1–p(TypeIIerror)

Influences on power
 Treatment effect > more effect more power
 Alpha level > larger alpha more power
 Twotailedvs.onetailedtest >onetailed more power
 Sample size >larger and more power

z test for one sample
 Used when the untreated population’s
 μ and σ are given
 The z statistic equation
 Effect size
 d= M  μ / σ

t test for one sample
 Used when the untreated population’s μ is given but no σ
 The t statistic equation
 Effect size
 ̂d=Mμ /s

Steps for t test
 1) set hypothesis
 2) set criteria
 3) calculate statistics
  sample variance
  sample standard deviation
  estimated standard error
  standard error
  t statistic
 4) make a decision
 5) Assess effect size
 6) report results

Effect Sizes
 d= 0.2 (small effect)
 d= 0.5 (medium effect)
 d= 0.8 (large effect)

Independent Samples ttest
 Between subjects design
 Simple experiment
 Simple quasiexperiment
 No matched groups!
 H0 : μ2 – μ1 ≤ 0
 H1: μ2 – μ1 > 0

Assumptions of independent measures ttest
  Groups are independent
  The two populations are normally distributed
  Homogeneity of variances
 σ1 squared =σ2 squared

effect size
A measure of this is intended to provide a measurement of the absolute magnitude of a treatment effect, independent of the size of the sample(s) being used.

Power
The probability that the test will correctly reject a false null hypothesis. That is, power is the probability that the test will identify a treatment effect if one really exists.

t distribution
The complete set of t values computed for every possible random sample for a specific sample size (n) or a specific degrees of freedom (df). The t distribution approximates the shape of a normal distribution.

Estimated Cohen's d
The estimate of the effect size when we don’t know the standard deviation of the population (we’ll use S instead)

independentmeasures/betweensubjects study
  uses a seperate sample to represent each of the populations or treatment conditions being compared
 > pooled variance

