Week 4

Question 1

What is an experiment?

Answer 1

an experiment there is manipulation of one variable, usually while keeping everything else constant

Question 2

What do experiments provide evidence for?

Answer 2

Experiments provide some evidence of cause and effect

Question 3

What is an observational study?

Answer 3

Illuminate patterns (correlation or associations between variables), but are unable to fully disentangle the effects of
measured explanatory variables and unmeasured confounding variables

Question 4

What is a confounding variable?

Answer 4

a variable that masks or distort the

causal relationship between measured variables in a study

Question 5

What is the limitation of observational studies?

Answer 5

An observational study might not be able to pull apart confounding variables because they will co-occur

Question 6

Is correlation symmetrical?

Answer 6

yes both variables will be correlated with each other

Question 7

Is Causation symmetrical?

Answer 7

no

– Smoking causes heart attacks, but heart attacks do not cause smoking

Question 8

What is the goal when designing a experiment?

Answer 8

to eliminate bias and reduce sample error.

Question 9

What is bias?

Answer 9

a systematic discrepancy between the
estimate you would obtain, IF you could
sample a population again and again, and the true population characteristic

Question 10

What is sampling error?

Answer 10

The difference between an estimate and the population parameter being estimated caused by chance

Question 11

What does reducing sampling error achieve?

Answer 11

increases precision

Question 12

What do control groups do?

Answer 12

Eliminates bias

Question 13

What does Randomization do?

Answer 13

Eliminates bias

Question 14

What does Blinding do?

Answer 14

Eliminates bias

Question 15

what does Replication do?

Answer 15

Reduces sample error

Question 16

what does Balance do?

Answer 16

Reduces sample error

Question 17

what does Blocking do?

Answer 17

Reduces sample error

Question 18

what do Sub-replicates achieve?

Answer 18

Reduces sample error

Question 19

Paired designs purpose?

Answer 19

Reduces sample error

Question 20

•Consistency of conditions purpose?

Answer 20

Reduces sample error

Question 21

What is a control group?

Answer 21

A simultaneous control group is a set of subjects that are treated in the same way as the treatment group in all ways except that the treatment is not applied

Question 22

What will happen with randomization?

Answer 22

Randomization will roughly equalize confounding factors

The confounding factors will still exist, but the effect will not be skewed

Question 23

What does Pseudo-randomization do?

Answer 23

almost always generates bias

Question 24

definition of blinding

Answer 24

Blinding is when either (or both) an experimenter and subject are unaware of treatment and control

Question 25

definition of replication

Answer 25

Replication is the term we use for running multiple treatments and controls at the same time

Question 26

What is the importance of having a greater number of replicates?

Answer 26

Studies with a greater number of replicates will have a larger n and a smaller standard error as a result

Question 27

What are replicates?

Answer 27

Replicates are not the number of plants or animals used, but the number of independent units

Question 28

What is the definition of balance?

Answer 28

A study design is balanced if all treatments and controls are equal in number

Question 29

What is the definition of Blocking?

Answer 29

Blocking involves assigning treatments randomly to replicates within 'blocks' A block of replicates share similar properties. Differences among treatments are evaluated within blocks.

Question 30

Paired design definition?

Answer 30

In a paired design the treatments are applied to replicates so that each replicate contains one spatially associated treatment and control.

Question 31

what does paired design do?

Answer 31

By pairing treatments and controls, a lot of environmental noise is reduced.

Question 32

What are Sub-replicates

Answer 32

Sub-replicates is the term for multiple samples taken from the same replicate.

Question 33

How are the results calculated when using sub replicates?

Answer 33

Usually the results of sub-replicates | are averaged to provide a final replicate result (data).

Question 34

Examples of sub replicates?

Answer 34

* Multiple leaves from a single plant * Multiple animals sampled at a single forest site * Multiple plugs from a single bacterial plate * Multiple aphids sampled from a single crop field

Question 35

What are the reasons you may want to use extreme treatment?

Answer 35

• Treatment effects easiest to detect when the effects are large • Small differences can be difficult to detect and require larger samples sizes

Question 36

What is the limitation of extreme treatments?

Answer 36

the effects of large dose may be different from the effects of smaller, more realistic doses

Question 37

What should extreme treatment be used for?

Answer 37

Use as first step in detecting an effect of one variable on another

Question 38

What is a factor?

Answer 38

single treatment variable

Question 39

Why might you want to do an experiment with more than one factor?

Answer 39

Factors night interact to affect the response variable in a way that you would not expect/see if you were to test each factor on its own

Question 40

What does an experiment with Factorial design?

Answer 40

examines all treatments combinations of two or more variables – It also can measure interactions between the treatment variables – Interaction between two variables mean that the effect of one variable depends upon the other variable

Question 41

What is Pseudoreplication?

Answer 41

Using sub-replicates instead of replicates in your analysis.

Question 42

What is the consequence of Pseudoreplication?

Answer 42

This inflates the n above what it should be and (sometimes vastly) increases the chances of significance.

Question 43

What is Co-correlation?

Answer 43

Two or more explanatory variables are correlating or 'cocorrelating' in a sample. This means the variables are (probably) measuring the same thing. i.e. 'lighter carrying' and 'cigarette carrying' are actually both measuring the behaviour of smoking.

Question 44

What is the problem with too many explanatory variable?

Answer 44

If you have n samples and n explanatory variables then your explanatory variables will explain all the variation in your data because there is one variable per data point. This isn't informative.