Flashcards in BIO 330 Deck (379)

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181

## test statistics

### used to evaluate whether the data is reasonably expected under the Ho

182

## P-value

### probability of getting the data, or something more unusual, given Ho is true

183

## reject Ho if

###
p-value ≤ alpha

less than OR equal to

0.049, 0.05

184

## Steps in hypothesis testing

###
1. State Ho and Ha

2. Calculate test statistic

3. Determine critical value or P-value

4. Compare test statistic to critical value

5. Evaluate Ho using sig. level (and interpret)

185

## Type I error

### Reject Ho, given Ho true

186

## Type II error

### Do not reject Ho, given Ho is false

187

## If we reduce alpha

### P[Type I] decreases, P[Type II] increases

188

## Experimental design steps

###
1.Develop clear statement of research question

2.List possible outcomes

3.Develop experimental plan

4.Check for design problems

189

## How to minimize bias

### control group, randomization, blinding

190

## How to minimize sampling error

###
replication- lare n lowers noise

balance- lowers noise

blocking

191

## to avoid pseudoreplication

### check df- obviously if its huge something is wrong

192

## Tukey-Kramer

### for 3 means: three Y bars, three Ho's; Q distribution; 3 row table w/ group i, group y, difference in means, SE, test statistic, critical q, outcome (reject/do not)

193

## Q-distribution

### symmetrical, uses larger critical values to restrict Type I error; more difficult to reject null

194

## Tukey-Kramer test statistic

###
q = Y_i(bar) - Y_j(bar) / SE

SE = √ MSerror(1/n1 + 1/n2)

195

## Tukey-Kramer testing

###
test statistic, q-value

critical value, q_α,k,N-k

k = # groups

N = total # observations

196

## Tukey-Kramer assumptions

###
random samples

data normally distributed in each group

equal variances in all groups

197

## 2 Factor ANOVA

### 2 Factors = 3 Ho's: difference in 1 factor, difference in 2nd factor, difference in interaction

198

## If interaction is significant

### do not conclude that factor is not

199

## Interaction plots

###
y-axis: response variable

x-axis: one of 2 main factors

legend for: other of 2 main factors (different symbols or colors)

2 lines

200

## interpreting interaction plot, interaction

### lines parallel: no significance in interaction

201

## interpreting interaction plot, b (data not on x-axis)

### take average along each line and compare the 2 on the y-axis, if they are not close then they are significant

202

## interpreting interaction plot, a (data on x-axis)

### x-axis: take average between the 2 dots (for each level of a), compare on y-axis, if they are not close they are significant

203

## control groups in an observational/experimental study will

###
reduce bias

will not affect sampling error

204

## correlation ≠

### causation

205

## correlation

###
"r"- comparing 2 numerical variables, [-1,1], no units, always linear

quantify strength and direction of LINEAR relationship (+/-)

206

## how to calculate correlation

###
r = signal/noise

signal= deviation in x and y together for every point (multiply each deviation before summing)

207

## correlation Ho

### no correlation between interbreeding and number of pup surviving their first winter (ρ = 0)

208

## determining correlation

###
test statistic: r/SE_r

SE_r = √ (1-r^2) / (n-2)

df = n-2

critical: tα,(2),df

compare statistic w/ critical

209

## df

###
n - number of parameters you estimate

correlation- you estimate 2

mann whitney- 0 parameters

210