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
