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
