exam 2

Question 1

descriptive, differences, correlation/regression, association projects (differences, projects)

Answer 1

descriptive - do not carry out hypothesis, the goal is to describe the situation (various statistical measures may be important), histogram, density and boxplots
differences - compares two or more sets of data (hypothesis will relate to differences you believe may exist), bar charts, side by side boxplots
correlation/regression - attempts to link variables (looking for strength and direction of links between variables), scatterplots, line plots
association - emphasis on links between variables that are categorical, bar charts, pie charts

Question 2

null and alternative hypothesis

Answer 2

we test the null hypothesis, data is gathered to test null
we do not prove the alternative hypothesis, the most we can do is find support for it

Question 3

possible outcomes from hypothesis testing

Answer 3

reject and fail to reject null - reject = null is not accurate, fail to reject = null is accurate
p-value - the probability that the null hypothesis is correct from the data gathered

Question 4

histogram

Answer 4

descriptive test

Question 5

boxplots

Answer 5

descriptive, difference (side by side)

Question 6

bar charts

Answer 6

differences, association

Question 7

scatterplots

Answer 7

correlation and regression

Question 8

line plots

Answer 8

correlation and regression

Question 9

pie charts

Answer 9

association

Question 10

histogram in r

Answer 10

hist(object)

Question 11

boxplot from object in r

Answer 11

boxplot(object)

Question 12

set scale of axis in r

Answer 12

ylim=c(0,0) xlim=c(0,0)

Question 13

add axis label and graph title in r

Answer 13

xlab = “Title”
ylab=”Title”
main=”Title”

Question 14

change colors of bars or boxes in r

Answer 14

col=”Color”

Question 15

popperian philosophy

Answer 15

we learn by being wrong, no amount of evidence can prove something is true (empirical falsification)

Question 16

testing a null/ reshuffling

Answer 16

to determine what no change would look like, create data that would be reasonable for the system (after plenty of research about what is realistic) to come up with more data

Question 17

level of probability that scientists use as a threshold for deciding how to interpret hypothesis

Answer 17

.05 p-value

Question 18

basic study set up for a t-test

Answer 18

create hypothesises, collect data, data must be normally distributed, each data point must be independent

Question 19

what happens to t when variables are changed

Answer 19

when t increases, mean difference increases, when t decreases, standard deviation increases, when t increases, n increases

Question 20

what test to do to determine if data are appropriate for t-test, how to interpret

Answer 20

find if the data are normal (boxplot or shapiro-wilk test)
greater than .05 = the data is normal and a t-test can be done

Question 21

t-test in r

Answer 21

t.test(object)

Question 22

one tailed vs two tailed t-test

Answer 22

one tailed - more power to detect directional effect (greater than or less than)
two tailed - shows evidence that the difference between means is greater than expected

Question 23

paired t-test

Answer 23

repeated observations collected for a single variable with 2 levels (differences between sample point 1 and sample point 2 are compared for the same sample unit)

Question 24

non-parametric test

Answer 24

use the rank of data and rank from smallest to largest, compare the ranks
mann-whitney (two sample) and wilcoxon (paired) tests

Question 25

import data as .csv into r

Answer 25

data<-read.csv(file.choose())

Question 26

how to take one column and create an object in r

Answer 26

object<- dataset$column

Question 27

plotting boxplots and histograms in r

Answer 27

boxplot(object)
hist(object)

Question 28

checking for normality in r

Answer 28

shapiro.test(object) OR
wilcox.test(object)

Question 29

basic code for t-test in r

Answer 29

t.test(object)

Question 30

how to tell if data are normal/not normal

Answer 30

parametric - do a shapiro wilk test
non parametric - do a mann-whitney or wilcoxon test

Question 31

how to deal with not normal data

Answer 31

can be log transformed OR use non-parametric tests

Question 32

how to use a non-parametric test

Answer 32

mann-whitney test - equivalent of 2 paired t test, compares the observed difference in mean of ranks to the maximum possible difference in the mean of ranks
wilcoxon test - matched pairs, compares the ranks of differences

Question 33

how to decide how much data is needed to collect

Answer 33

preliminary sampling - small study to refine and evaluate sampling size, acceptability, feasibility, and cost of larger study (determines problems and best methods)
dummy data - learn everything and then make up what you think are plausible data
primary literature investigation - learn everything you can about your system and others like it (what are other people doing)
update analysis as you collect data

Question 34

when do we have independent replicates and when do we not

Answer 34

pseudoreplication - if replicates are “tied” to each other in some way
all independent data points must have no connection to other data points

Question 35

simple pseudoreplication

Answer 35

only a single replicate per treatment and subsamples are collected from each area

Question 36

sacrificial pseudoreplication

Answer 36

experimental units are replicated

Question 37

temporal pseudoreplication

Answer 37

only a single replicate per treatment and subsamples are collected from it repeatedly over time

Question 38

phylogenetic pseudoreplication

Answer 38

closely related individuals are the units being sampled (seeds, tadpoles, insect larvae)

Question 39

technical replication

Answer 39

different observers or instruments are used for different parts of the experiment

Question 40

converting continuous variables to catagories

Answer 40

can limit the amount of data visible, can give an inaccurate and varying perspective of the results

Question 41

true positive (p-value)

Answer 41

when Ho is true and we fail to reject

Question 42

true negative (p-value)

Answer 42

when Ho is false and we reject it

Question 43

false positive (p-value)

Answer 43

when Ho is true and we reject it

Question 44

false negative (p-value)

Answer 44

when Ho is false and we fail to reject it