BIO 330 Flashcards Preview

Biology > BIO 330 > Flashcards

Flashcards in BIO 330 Deck (379)
Loading flashcards...
151

Hypothesis test steps

State Ho, Ha
calculate test statistic
determine critical value of null distribution (or P-value)
compare tests statistic to critical value (or P-value to sig. level)
evaluate Ho using alpha

152

why use alpha = 0.05

balances Type I error and Type II error

153

why are Type I and II errors conceptual

we don't know whether or not Ho is actually true

154

paired t-test is a type of

blocking

155

where does pseudoreplication happen/become a problem

data analysis stage, doesn't happen at data collection stage (subsamples)

156

ANOVA maintains

P[Type I Error] = alpha

157

ANOVA, Y bar

grand mean, main horizontal line, test for differences between grand mean and group means

158

ANOVA, Ho: F-ratio =

~1

159

ANOVA, if Ho is true, MSerror

= MS groups; same variation within and btw goups

160

ANOVA, MSgroup > MSerror

more variation between groups than within

161

ANOVA, test statistic

F-distribution, F_0.05,(1),MSgroup DF, MSerror DF = critical value
compare critical value to F-ratio
this is a one sided distribution we are looking for whether F-ratio is bigger than critical value (strictly)

162

ANOVA, F-ratio > F-critical

Reject Ho.. at least one group mean is different than the others

163

ANOVA, quantifying variation resulting from "treatment effect"

R^2 = SSgroups/SStotal
R^2 [0,1]

164

ANOVA, high R^2

more of the variation can be explained by the treatment, usually want at least 0.5

165

ANOVA, R^2 = 0.43

43% of total variation is explained by differences in treatment

166

ANOVA, R^2 = low values

noisy data

167

ANOVA assumptions

Random samples from populations
Variable is normally distributed in each k population
Equal variance in all k populations

168

ANOVA unmet assumptions

large n, similar variances-- ignore
variances very different-- transform
non-parametric-- Kruskal-Wallis

169

ANOVA, which group(s) were different

Planned or Unplanned comparison of means

170

Planned comparisons of means (ANOVA)

comparison between means planned during study design, before data is obtained; for comparing ONE group w/ control (only 2 means); not common

171

Unplanned comparisons of means (ANOVA)

comparisons to determine differences between all pairs of mean; more common; controls Type I error

172

Planned comparison calculations (ANOVA)

like a 2-sample t-test
test statistic: t =(Ybar1 - Ybar2)/SE
SE= √ MSerror (1/n1 + 1/n2)
note that we use error mean square instead of pooled variance (as in a normal t-test)
df = N-k
t critical= t0.05(2), df

173

Unplanned comparison of means (ANOVA)

Tukey-Kramer

174

why do you need to know what kind of data you have

determines what kind of statistical test you an do

175

left skew

mean < median
skew 'pulls' mean in direction of skew

176

C.I. notation

95% CI: a < µ < b (units)

177

accept null hypothesis

NEVER!!!
only REJECT or FAIL TO REJECT

178

why do we choose alpha = 0.05

it balances TIE and TIIE which are actually conceptual, since we don't know if Ho is actually true or not

179

standard error or estimate

standard deviation of its sampling distribution; measures precision of the estimate

180

SD vs. SE

SD- SPREAD of a distribution, deviation from mean
SE- PRECISION of an estimate; SD of sampling distribution