Week 4 - Experimental design

Question 1

population

Answer 1

all the individuals units of interest.

Question 2

sample

Answer 2

subset of units taken from the population.

Question 3

sampling error

Answer 3

sampling error is the chance difference between an estimate and the population parameter being estimated caused by sampling.

Question 4

good samples

Answer 4

Ideally estimate is accurate or unbiased
Ideally precise

Question 5

random sample

Answer 5

each member of the population has an equal and independent chance of being selected.
Minimised bias and makes it possible to measure the amount of sampling error

Question 6

how to create a random sample

Answer 6

Create a list of every unit in the population and give each a number
Decide on the number of units to be sampled (n)
Use a random number generator
Sample the units whose numbers match those produced by the generator

Question 7

convenience sampling and issues

Answer 7

a collection of individuals that are easily available.

Volunteer bias

Question 8

confounding variable

Answer 8

unmeasured variable that changes in tandem with the measured variables and makes a false relationship appear.
Correlation does not equal causation
Gender might be a confounding variable if not accounted and controlled for in experimental design

Question 9

experimental artifact

Answer 9

bias in a measurement produced by unintended consequences of experimental procedures.

Question 10

clinical trial

Answer 10

experimental study where two or more treatments are applied to humans.

Question 11

ways to reduce bias

Answer 11

Control group
Randomisation - random assignment of treatments (pseudo-randomisation causes bias almost always)
Blinding - single and double

Question 12

ways to reduce sampling error

Answer 12

Replication (repeat with multiple participants)
Balance - all treatments have equal sample size (might result in very small overall group)
Consistency of conditions
Sub sample - multiple leaves on a single plant and averaged as one data point
Blocking (grouping of experimental units that share the same location or time of measurement or other properties. Within each block, treatments are randomly assigned to experimental units)
Randomised block design - paired design but for more than two treatments
Paired design - treatments are applied to same sampling unit (two types of fertiliser tested in the same area of the field)

Question 13

experimental unit

Answer 13

individual or group of individuals.

Question 14

making a plan

Answer 14

Develop a clear research question
List possible outcomes
Develop an experimental plan
Keep the design as simple as possible
Check for common design problems
Is the sample size large enough
Discuss the design with other people

Question 15

extreme treatments

Answer 15

Start with an extreme treatment to see if an unknown variable has any effect
Then do a realistic experiment with lower levels

Question 16

factor

Answer 16

single treatment variable whose effects are of interest to the researcher.

Question 17

factorial design

Answer 17

all treatment combinations of two or more variables. Can measure interactions between treatment variables
Interaction between two or more explanatory variables means that the effect of one variable depends upon the state of the other variable.
Effect of gender and effect of medicine and is there an interaction between these two things?

Question 18

matching

Answer 18

every individual in the treatment group is paired with a control individual that has similar values for the suspected confounding variables.