BIO 330 Flashcards Preview

Biology > BIO 330 > Flashcards

Flashcards in BIO 330 Deck (379)
Loading flashcards...
121

good skewness range for normality

[-1,1]

122

normal quantile plot

QQ plot
compares data w/ standardized value, should follow a straight line

123

right skew in QQ plot

above line (more positive data)

124

Shapiro-Wilk test

works like Hypothesis test, Ho: data normal
estimate pop mean and SD using sample data, tests match to normal distribution with same mean and SD
p-value < alpha, reject Ho (don't want to reject)

125

testing normality

Histogram
QQ plot
Shapiro-Wilk

126

normality tests sensitive

especially to outliers, over-rejection rate
sensitive to sample size
large n = more power

127

testing equal variances

Levene's test

128

Levene's test

Ho: sigma1 = sigma2
difference btw each data point and mean, test difference btw groups in the means of these differences
p-value < alpha reject (don't want to reject)

129

how to handle violations of test assumptions

ignore it
transform data
use nonparametric test
use permutation test

130

when to ignore normality

CLT- n >30 ----means are ~normally distributed
depends on data set though
can't ignore normality and compare one set skewed left with one skewed right

131

when to ignore equal variances

n large, n1 ~ n2
3 fold difference in SD usually ok

132

if can't ignore violation of equal variances

Welch's t-test- computes SE and df differently

133

most common transformations

log, arcsine, square-root
log- only in data all > 0

134

nonparametrics

assume less about underlying distributions
usually based on rank data
Ho: ranks are same btw groups
sign test (instead of t test)

135

sign test

compares median to median in Ho
each data pt- record whether above (+) or below (-) the Ho median

136

if Ho is true in sign test

half data will be above Ho, half will be below

137

sign test p-value

use binomial distribution-- probability of getting your measurement if Ho true, compare to alpha

138

binomial

P[Y≤y] = Σ(n choose y)(p)^y(1-p)^n-y

139

Mann-Whitney U-test

compare 2 groups using ranks
doesn't assume normality
assumes distributions are same shape
rank all data from both groups together, sum ranks for individual groups

140

Mann-Whitney U-test equation

U1 = n1n2 + [(n1(n1+1)/2] - R1
U2 = n1n2 - U1

141

interpreting Mann-Whitney U-test

choose larger of U1, U2 (test statistics)- compare to critical U from U distribution (table E)
note that Ucrit = U_alpha,(2 sided), n1, n2
used n1, n2 not DF
U < Ucrit d.n.r. Ho (2 groups not statistically different)

142

why Mann-Whitney doesn't use DF

not looking at estimating mean/variance, just comparing the shapes

143

problem with non-parametrics

low power- P[Type II] higher-- especially with low n
ranking data = major info loss
avoid use
Type I not altered

144

comparing > 2 groups

ANOVA - analysis of variance
Ho: µ1 = µ2 = µ3 = µ4....

145

why use ANOVA

multiple t-tests to compare >2 groups increase Type I error- more tests = higher chance of falling within alpha

146

P[Type I]

1 - ( 1 - alpha ) ^N
N is number of t-tests you do
ex. 5 groups- 10 unique tests- P[TI] = 0.4

147

ANOVA tests

is there more variation btw groups than can be attributed to chance- breaks it down into: total variation, btw group variation, within group variation
maintains P[TI] = alpha

148

between-group variation

effect of interest (signal)

149

within-group variation

sampling error (noise)

150

2x2 ANOVA design

take 2 different variables-- look at all combinations and see if any effects between them in all directions
2 variables w/controls = 8 options