Inferential Statistics

Question 1

Parametric assumptions

Answer 1

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

Question 2

Normality

Answer 2

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

Question 3

test of normality

Answer 3

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

Question 4

Histogram

Answer 4

frequency of scores on Y, individual scores on X

you can see most frequent occuring score.

Question 5

QQ plot

Answer 5

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.

Question 6

Homogeneity of Variance

Answer 6

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

Question 7

arcsine and rau

Answer 7

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.

Question 8

positive skew

Answer 8

tail is dragged out in positive direction

Question 9

welch correction

Answer 9

for unequal variances in two-sample t-tests

default in r

Question 10

non-parametric

Answer 10

doesn’t assume normality

Question 11

logistic regression

Answer 11

powerful

using all data, not means

Question 12

independent observations

Answer 12

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

Question 13

group comparisons - t test

Answer 13

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)

Question 14

t test comparing signal to noise

Answer 14

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

Question 15

single sample

Answer 15

one group vs baseline

Question 16

paried samples t test

Answer 16

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

Question 17

independent samples

Answer 17

compares two separate groups of observations (pooled SD)

Question 18

increase effect size

Answer 18

low standard deviation

high difference between the means

Question 19

error bars

Answer 19

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 ***

Question 20

confidence intervals

Answer 20

mean +/- the t criteria

and does not include 0

Question 21

ANOVA

Answer 21

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

Question 22

ANOVA factors

Answer 22

One way
two way
three way

Question 23

ANOVA Levels

Answer 23

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

Question 24

2x2x3

Answer 24

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

Question 25

types of assignment

Answer 25

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

Question 26

p value-

Answer 26

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

Question 27

one tail or two

Answer 27

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

Question 28

significance

Answer 28

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.

Question 29

type I error vs type II

Answer 29

first error was believe him (there was no wolf)

the second error was not believing him (there was a wolf)

Question 30

pearson correlation (r)

Answer 30

relationship between two interval ratio measures

-1 to 1d

Question 31

spearman rank-order correlation

Answer 31

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

Question 32

chi-square and contingency coefficient

Answer 32

association between nominal variables

Question 33

regression

Answer 33

predictive value of association

Question 34

multiple regression

Answer 34

strongest comination of IV that predict a DV

Question 35

what to include in paper?????

Answer 35

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.