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Flashcards in Inferential Statistics Deck (35):
1

Parametric assumptions

Normality
Homogeneity of Variance
Independence of observations (not relatied onless repeated measures)

2

Normality

sampling distribution of the mean
DV like working memory
take a group and get a sample mean
each time you get a sample you have a distrubuation with a mean.
if you did this thousands of times, all the means would appear normal

3

test of normality

Kolmogorov-Smirnov test and Shapiro-wilk test
statisticians say this is a waste of time. but you shouldl look at your data to make sure there are no major differences in normality.
(skewness or kurtosis)
or just plot your data. Histogram or QQ plot

4

Histogram

frequency of scores on Y, individual scores on X
you can see most frequent occuring score.

5

QQ plot

quantile is a type of percentile- how much data are included in this value.
how much data are in the different regions (you want it to be a straight line, circles are data points, 16% here and here and you will need less in the middle.

same info from histogram but its it on the line or not.

6

Homogeneity of Variance

homoscedasticity
do the different groups have the same variance
most tests are robust to violations as long as the grop sizes are equal. (levenes)

7

arcsine and rau

arcsine - uses radians
rau - more math - but becomes percent correct

could they have done worse than 0% if ranking extended that far? could they have performed the 100% better than someone else.

8

positive skew

tail is dragged out in positive direction

9

welch correction

for unequal variances in two-sample t-tests
(default in r)

10

non-parametric

doesn't assume normality

11

logistic regression

powerful
using all data, not means

12

independent observations

two SNR, if its independent T test then only one person can contribute to each

if same person did do both you can run repeated measures analysis

when one person performs two different conditions then there is something related
can you predict snr .1 from snr 2? yes

13

group comparisons - t test

why use t test?
z is normal gaussian curve (mean 0 SD 1)
t is shifted slightly, not standard gaussian and we use because its better for small sample sizes when we dont actually know the true population mean and SD
(humans are not something we typically know the underlying distribution)

14

t test comparing signal to noise

what is the effect, divided by the random variation????

15

single sample

one group vs baseline

16

paried samples t test

compares two matched paired sets of observations (single-sample of paired differences)

17

independent samples

compares two separate groups of observations (pooled SD)

18

increase effect size

low standard deviation
high difference between the means

19

error bars

confidence intervals - a quarter of the length of the overall length can have statistical difference

SEM - need to have at least half the length of the error bar difference to show an effect.

thats why use ***

20

confidence intervals

mean +/- the t criteria
and does not include 0

21

ANOVA

analysis of variance, parametric statistic
mean difference relative to variability.
difference in the amount of variability between groups vs within groups

main effects . - effects of each IV and how do they interact with one another

22

ANOVA factors

One way
two way
three way

23

ANOVA Levels

each factor can have 2+ levels (age factor - young and old are the levels)

24

2x2x3

three factors (three IV)
young and old
college grad vs not
speech rate (fast med slow)

25

types of assignment

random (true exp)
non-random (quasi experiemntal and non exp)
- in tact groups, matched group design,

26

p value-

probability that your observed results (or a more extreme result) came from the distribution or your null hypothesis.

27

one tail or two

only use one tail if obtaining a result in the other direction is impossible or interpretable.

28

significance

significance is not importance
not more or less sig.... like you cant say passed the exam more than stacey. this idea would be effect size.

29

type I error vs type II

first error was believe him (there was no wolf)
the second error was not believing him (there was a wolf)

30

pearson correlation (r)

relationship between two interval ratio measures
-1 to 1d

31

spearman rank-order correlation

strength/ direction of assoication between two ranked (ordinal variables)

32

chi-square and contingency coefficient

association between nominal variables

33

regression

predictive value of association

34

multiple regression

strongest comination of IV that predict a DV

35

what to include in paper?????

pearson
- r, pvalue
- some include df stats for each vairable and r2
type of analysis conducted, two tailed perison threshold for sig.


Working memory span and word identification were significantly
positively correlated (r = 0.41, p < .05).

There was a significant positive correlation between working memory span (M = 3.34, SD = .10) and word identification (M = 9.43, SD = 1.03), r(23) = 0.41, p = 0.42.