midterm Flashcards
what is the p value
the significance level
- represents the portion of data sets that would yield a result as extreme or more extreme than the observed result if the null hypothesis is true
p <= ⍺ vs p > ⍺
p <= ⍺ : reject H0
p > ⍺ : retain H0
what does it mean when H0 is rejected
there is a statistically significant effect in the population
what does a confidence interval mean?
if we repeat our experiment a bunch of times, our results will fall in that interval a certain percentage of the time
ex. 95% confidence interval meals 95% of the time, our results will fall in the 95% interval
how do we form confidence intervals?
{(1-⍺)x 100}%
ex. if alpha=.05 = 95%Ci, .01=99%CI
what happens to CIs as sample size increases
our estimates become more precise and CIs become narrower
what happens to CIs as ⍺ decreases?
CIs become larger or wider
effect sizes for pearson, correlation ratio squared, cohens d:
small:
pearson (r): 0.10
correlation ratio squared R^2: 0.01
cohens d: 0.2
medium:
pearson (r): .30
R^2: 0.09
d: 0.5
large:
r: .50
R^2:0.25
d:0.8
Type I and Type II errors
Type I: reject H0 when it is true - false positive
Type II: retain H0 when it is false - false negative
what is ⍺ and β in errors in hypothesis testing
alpha: probability of committing type I error
beta: probability of committing type II error
what is power?
the probability of correctly rejecting a false H0
1 - β
alpha and beta relationship
higher alpha means lower beta - less conservative test power to reject null is higher
buuuut also higher probability of false positive - good and bad
assumptions of a single mean t test
variable, X, is normally distrubuted
independence of observations
t stats follow t distribution - approaches normal distrubtions as sample size (df) get bigger
between subjects design
independent samples - each participant only goes through one of two conditions in an experiment
correlated samples
dependent subjects, paired samples, repeated measures - participants go through both conditions
independent samples t test assumptions
- dependent variable normally distributed
- standard deviations of both populations are the same - homogeneity of variance
- each subject is independent