300

Question 1

non-parametric alternative: Paired T-test

Answer 1

Wilcoxon signed-rank test

Question 2

non-parametric alternative: Two sample T-Test

Answer 2

Mann-Whitney U test

Question 3

non-parametric alternative: One-way ANOVA

Answer 3

Kruskal-Wallis test

Question 4

non-parametric alternative: Repeated-measures ANOVA

Answer 4

Friedman test

Question 5

non-parametric alternative: Correlation

Answer 5

Spearman’s rank correlation

Question 6

non-parametric alternative: Chi-square test for independence

Answer 6

Fisher’s exact test

Question 7

non-parametric alternative: Simple linear regression

Answer 7

rank-based regression

Question 8

Chi-square contingency analysis test steps and when to use:

Answer 8

When you have two or more independent samples with categorical outcomes. (Snakes with or without bands from two different areas).

Write out contingency table

Calculate expected counts - (Row total * column total)/Grand total

With expected value, calculate chi square.

Df = rows - 1 * columns - 1

Chi-square = n(ad-bc)^2/(a+b)(c+d)(b+d)(a+c)

Question 9

Chi-square Goodness of fit steps and when to use:

Answer 9

Test if observed frequencies match a theoretical model - 3:1 mendelian ratio.

Find expected counts

compute chi-square

df= categories -1

Question 10

One sample t-test steps and when to use:

Answer 10

Comparing a sample mean to a known constant
(observed body temp to expected body temp)

find standard deviation and mean.

Calculate t

Question 11

How to compare means with unequal variance

Answer 11

Welch’s T-test

Question 12

Paired t-test steps and when to use:

Answer 12

Compare mean of differences between a pair to null hypothesis value.
(cooling constant of mice, reheated vs fresh)

Find standard deviation and mean of difference between pairs.

calculate T

Question 13

2 sample T-test steps and when to use:

Answer 13

Comparing means of two independent groups.

Need to find s^2 for both groups

pooled variance

SE

Then calculate T using means and SE

Assumptions: Normality in both groups and equal variances

Question 14

Single factor ANOVA steps and when to use:

Answer 14

Comparing means among >2 groups.

Assumes normality and equal variances.

Compute grand mean: sum of all data/N

Compute MS Error and MS group to find F

df Error = N - k
dfGroup = k-1

Question 15

Correlation

Answer 15

Test strength & direction of linear relationship between 2 numeric variables.

Find r and SEr

t=r/SEr

df=n-2

Question 16

regression

Answer 16

Predicting y from x

Linear assumes the relationship can be described by a line.
Logistical is for categorical variables.

find b

find intercept (alpha)

from that get equation for y

Find SEb

t=b/SEb

Question 17

Test to determine if normally distributed

Answer 17

Shapiro-Wilkins test

Question 18

M

Question 19

Chi-square goodness-of-fit assumptions

Answer 19

Independent observatuions
If expected counts are small, use an exact test.
Each observation falls into only one category

Question 20

Chi-square contingency asumptions

Answer 20

Data are counts/frequencies
Independent observations
No cell should have 0 expected count
Small counts -> fisher

Question 21

One-sample t-test assumptions

Answer 21

Response variable is numerical and continuous
dample is independent and random
Variable is normally distributed in the population

Question 22

Two-sample t-test assumptions

Answer 22

Response variable is numerical and continuous
Samples are independent
Both groups normally distributed
Variances are equal, if not - Welch’s t-test

Question 23

Paired T-test assumptions

Answer 23

random sample of pairs, the differences are normally distributed

Question 24

ANOVA assumptions

Answer 24

All samples are random samples from populations that are normally distributed with equal variance.

Question 25

Pearson Correlation assumptions

Answer 25

Linear relationship between variables for all values of x, y is normally distributed. For all values of y, x is normally distributed w/ equal variance

Question 26

What is the central limit theorem

Answer 26

The sum (or mean) of a large number of measurements randomly sampled from a non-normal distribution is approximately normally distributed.

Question 27

Three features in experiment to reduce bias

Answer 27

control randomization blinding

Question 28

F-test

Answer 28

Used to compare variances