Test 2 Flashcards
(65 cards)
1
Q
Making a prediction steps
A
-compose hypothesis
-generate predictions
-test predictions
-evaluate hypotheses
MUST MAKE TESTABLE PREDICTIONS
2
Q
Deductive reasoning
A
- starts with a theory, test, revise
- top down approach
- general–>specific
3
Q
Inductive reasoning
A
- starts with observations, form a theory
- specific–>general
- can be falsified with contradictory evidence
4
Q
Lakatos (1978)
A
- individual tests are risky and arbitrary
- should have multiple competing hypotheses
5
Q
Kuhn paradigm (1970)
A
- not linear discovery, but series of paradigm shifts
- scientists aren’t objective but rather come to a consensus
6
Q
Manipulative data
A
-when you’ve changed something and gather information
7
Q
Observational data
A
-when you observe what’s happening in a system
8
Q
A priori
A
Ahead of time, before collection of data
9
Q
measures of central tendency
A
mean
median
10
Q
t test equation
A
t = x - µ / SEM t = current mean - comparison mean/ standard error of the mean
11
Q
standard error of the mean
A
variance/n
12
Q
Confidence interval
A
- use confidence interval to calculate sample size
- also need variance, alpha,t,df
13
Q
t test assumptions
A
- Independent
- random sample
- normally distributed
- equal variances (homogeneity)
- must test these before any stats can be done!
14
Q
How can we test for normality?
A
shapiro-wilk
kolgomorov-smirnov
15
Q
Testing for variance?
A
Levene’s test for equality of variances
-similar bell curve shape
16
Q
What if normal distribution, but unequal variances
A
indep t test with equal variances not assumed
17
Q
not normal dis, but similar variances
A
- non parametric Mann Whitney u test
- doesnt consider parameter of calculated mean
- ranks data and calculated u stats, based on difference in rankings
18
Q
data not independent
A
paired t test
19
Q
Steps for t tests
A
- identify question
- state H0 and Ha in respect to your samples
- alpha level and direction of relationships
- choose test after exploring data to understand if it complies
20
Q
Statistical tests vary in:
A
- number of IVs and DFs
- levels of measurements (ordinal, continuous, category)
- variable: univariate tests, vectors, matrices in multivariate tests (scalars)
- role of variables: DVs, IVs, Covariates?
21
Q
Univariate
A
single dependent variable
22
Q
Multivariate
A
employ one or more dependent variable
23
Q
Vectors and matrices
A
vectors- variables with magnitude and direction
matrices–2D array of vectors
24
Q
Power
A
-important to high enough power to detect an effect
need to know:
-effect size
-alpha
-sample size
-data dispersion
Amount power = % chance can detect an effect
OR probability of not committing type II error (false negative rate)
25
Effect size
- power
- alpha
- n
- s data dispersion
- known
26
G power
-allows you to calculate the sample size needed for univariate and multivariate tests
27
post hoc power calc
- usually when your results were almost significant
| - often in poor taste
28
Linear relationship
-predictor and response
-bivariate = x and y
positive, negative and no relation = zero
29
scatterplot
-scatter diagram is graphical method to display relationship between two variables
30
Fitting a line
-least squares method.
-distance from potential line (residuals) squared and added up for all points to try to get lowest number possible
-always passes through the mean of y and x
WHY
to convert a value
standardize: calibration curve!
31
regression significance
can we distinguish line with slope from line with no slope
| zero slope or no relation is our null
32
R^2
coefficient of determination
- how much variation in y is determined by x
- want 1 or -1
33
Assumptions of regression
- each x and y are independent and random
- normal distribution of x values
- homogenity
- linear relation
- measurements of x are free of error or small compared to y (error will make a relation hard to understand)
34
Applications of Regression line
-can be used to predict
-R measure of strength of linear assocation between x and y
-R is NOT sqrt(R^2)
-Want 1 or -1
R > 0 direct linear
R
35
Spearman Rank Correlation
- doesn't meet normality
- homogenity of variance
- rank correlation also used when one or both consists of ranks
- can also have multiple values y for x
36
Parametric tests
-indep and paired t test
-correlation analysis
-linear regression
ANOVAS
37
Non Parametric tests
(also have their own assumptions!)
- Mann Whitney u
- Spearman Rho
38
Transformation
-take an abnormal distribution to normal
-there's a number of ways to do this depending on original distribution
-WONT MAKE UP FOR POOR SAMPLING specifically non random sampling, very sensitive to outliers
-KNOW YOUR LIT/FIELD
prepare to defend your choice
39
Log Transformation
heterogeneity of variance (base 10 or natural)
40
Square Root Transformation
heterosadastic variance ( data with non-constant variance) commonly used on count data
41
Arcsine transformation
-binomial dis
-yes/no
-proportions or percentages
-sqrt of a number
radians range from 0 to 1
42
Back transform
-even though you've transformed, means nothing to readers, have to go backwards for writing it up
43
Outliers
- data value different from majority
- need to report and state why you throw them if you trim your data set
- Need to think about them
- can't discard due to inconvenience
- rerun analysis without outlier to see if its the same
- run an rank test? categories?
- transforming may help
44
Lost, corrupted, removed data
- reduces sample size
| - small size decreases power and increases chances of extremes
45
quantitative data
discrete data
| 3 of something
46
Continuous data
3.14579 of something
47
categories
I am a human
| convert data into bins
48
Types of data can be divided into groups
race age sex
-put into contingency table
-categorical variables
-chi square analysis
must always use frequencies and see how it compare to expected
can use models! Mendelian genetics used Hardy Weinberg
49
Chi square things
50
Odds ratio
odds success/odds failure
51
Mosaic plot
graphical way to look at frequencies
- column = "treatment"
- row variable = "response"
52
ANOVA
- statistical test that exploits variance (s^2)
| - uses normally dis sets to compare differences between groups
53
Basic one way ANOVA
```
Two variables:
-categorical
-quantitative
Question:
Do the means of the quantitative variable depend on which category the individual is in?
IF ONLY 2 values
2 sample t test
but you can have 3 or more :)
-determines p value from f statistic
```
54
What does ANOVA do?
Tests these hypotheses:
1. means of the groups are equal (H0)
2. not all means are equal (Ha)
* doesn't tell us which differ, have to follow up with post hoc testing
55
ANOVA assumptions
-each group is approx normal
check graphically, or with normality tests. Can withstand some weirdness but not crazy outliers
-STDEVS are approx equal between each group
ratio of largest to smallest sample's stedv should be less than 2:1
Levene's test takes care of this
56
ANOVA notation
n = number of total individuals
I = number of groups
x = individual
X bar = mean for entire data set
57
How does one way ANOVA work?
measures variation
- between groups (group mean and overall mean)
- within groups (value between value and mean of group)
58
ANOVA f statistic
ratio of between group mean square variation/mean square within group variation
between/within
MSG/MSE
59
R^2 statistic
sum of squares between/ sum of squares total
| SSB/SST
60
If ANOVA groups don't have the same means
- compare in twos: pairwise using two sample t test
| - need to adjust p value threshold because multiple tests same data
61
Turkey's Pairwise comparisons
- if family error rate is 0.05 then
| - individual alpha = 0.0199 w/ 95 % CI
62
ANOVA data not normal?
kruskal-Wallis Test
| nonparametric procedure used to test the claim that 3+ indep samples come from pops with the same distribution
63
kruskal-Wallis Test
- STRONGER hypothesis than ANOVA which only compares means
- samples are simple random samples from 3+ pops
- data can be ranked
- principle is dumping all the data together and seeing if its a normal dispersion
- large values of H indicate Ri (sum of ranks of samples) are different than expected
- If H is too large then we reject the null
- K-W is always right tailed
64
K-W test critical values
- 3 populations or sample size of 5 or less, value from K-W table
- 4 or more or sample size from one pop is greater than 5, value is chi^2
65
K-W hypothesis test steps
step 0: samples are indep random, data can be ranked
step 1: box plots to compare data
step 2: hypotheses.
H0 data dis is the same
H1 data dis is not the same
step 3: rank observations smallest to largest
step 4: level of signifigance--either K-W or chi^2
step 5: compute test stat
step 6: compare critical values
test stat must be bigger than crit value
