PSYC 301 Flashcards
simplest explanation for difference is
chance
independent samples t test equation
t=x1-x2/SE
why? assuming pop = 0
what’s the idea of SE
accuracy or precision of our estimates
when it’s small, our estimates are probably pretty good
as sample size increases, SE decreases
Abelson’s MAGIC criteria
M- Magnitude
A- Articulation
G- Generality
I- Interestingness
C- Credibility
what is s (standard deviation)
dispersion of scores around the means
a bigger S (SD) means
means a bigger spread; worse estimate
focus of NHST
is on a qualitative decision: does a systematic difference exist or is the different merely a function of chance?
NHST is a method for
deciding a difference likely exists, but does not speak to the size of that difference
bayesian statistics argues that
just bc the null is unlikely for our data, does not necessarily mean the data are likely to be drawn from a population where our systematic difference is true (alt is true)
in bayesian statistics, we calculate a?
Bayes factor
(ratio of the liklelihood of the alt hypothesis relative to the liklihood of the null hypothesis
what bayes factor is considered moderate and strong evidence for alternative hypothesis more likely than null
3 moderate (threshold for starting claims)
10 strong
bayesian approach offers
an alternative method for assessing the viability of our random chance explanation vs a systematic explanation
bayesian statistics has same problem as NHST which is
“does increase in confidence in the alternative relative to the null really translate into magnitude of the effect and how do I interpret that”
- doesn’t tell you whether it’s big or small which is the same issue with NHST
raw effect sizes
- not used much in psych
- look at the size of the difference between the two means and treat that as an index of magnitude
- used in econ with money which is a meaningful benchmark
raw effect sizes are helpful when
when the outcome variable of interest (DV) is on a metric that is meaningful and readily interpretable in light of some clear criteria
raw effect sizes are problematic when
- the outcome variable is not easily interpretable with respect to specifiable criteria
- one needs to compare effects with outcome variables that are on different metrics
standardized effect sizes indices names
cohen’s d and ___
independent samples t test cohens d formula
ds = x1-x2/pooled s
pooled S equation
Pooled S=
√(n1-1) S2 + (n1-1) S22/(n1+n2-2)
ds ____ as the mean difference ____ and the standard deviations ____
increases increases decrease
ds is not influenced by
sample size
cohens d is sensitive to 2 properties of the data
- differences of means (2 rly far apart bigger than closer together)
- standard deviation (as SD gets rly small, effect sizes get bigger)
cohens d is an index for
for how distinct 2 groups are from each other
ds has a minimum value of __ and an upper boundary of ___
min value of 0 and no upper boundary
ds can be interpreted as the % of the SD
0.5- difference between the means is half the size of the dependent variable’s SD
1.00- indicates the difference is as big as the SD of the dependent variable
2.00- indicates a mean difference twice the size of the standard deviation of the DV
ds guidelines (cohen’s d guidelines)
0.2 small
0.5 medium
0.8 large
dav equation
dav = D/ avg. S
what does dav ignore and drm takes into account
ignores the magnitude of correlation between sets of observations
drm equation
look @ ipad
___ will tend to be more similar to ___ than ___ except when r is low and the difference between SD are large
dav will tend to be more similar to ds than drm except when r is low and differences between SDs are large
___ is more conservative than ___ but is considered overly conservative when r is large
drm dav
pearson r coefficient r is what
r is the strength of association between variables
r can be calculated to express what
r can be calculated to express the strength and direction of association between two continuous variables and also the relationship between a dichotomous variable (ex. membership in one of two groups) and a continuous variable (ex. a dependent variable)
biserial correlation
r express the relationship between a dichotomous variable (ex. membership in one of two groups) and a continuous variable (ex. a dependent variable)
in this context r can be conceptualized as the strength of association between membership in one of the two groups and scores on the dependent variable or when squared it expresses the proportion of variance in the DV accounted for by group membership
interpreting r as an effect size index
r ranges from -1.00 to 1.00 with .00 indicating no association
cohen’s guidelines for r
.10 (small)
.30 (medium)
.50 (large)
if r gets bigger, our cohen’s d gets ___
smaller
will adjust down the more correlated the two scores are
large effect sizes do not directly imply practical significance, why?
- metric can be hard to interpret without reference to more concrete reference criteria
- durability of an effect might also be relevant in addition to its size
- cost/benefit analysis also can determine practicality