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