Week 7

Question 1

What are the advantages of the ‘Scientific Method’?

Answer 1

• enables evaluation of theories/findings using ‘tangible’ concepts and methods.
• can be validated by ‘replication’
• every step of the research process can be scrutinised and improved upon.
• findings can be compared - progress can be observed.

Question 2

What is a hypothesis?

Answer 2

A clear and testable statement about a predicted relationship between two or more variables.

Question 3

What should a good hypothesis include?

Answer 3

• based on theory and allow specific predictions to be made about patterns that will be observed in data.

Question 4

What is the alternative hypothesis?

Answer 4

Assumes an effect will be present based on theoretical constructs, results of previous studies and past experiences/observations.

Question 5

What is a null hypothesis?

Answer 5

States there will be no relationship between the two variables.

Question 6

Correlational design H1 and H0:

Answer 6

H1 - there is a relationship between A and B
H0 - there is no relationship between A and B

Question 7

Experimental design H1 and H0:

Answer 7

H1 - there is a difference between A and B
H0 - there is no difference between A and B

Question 8

Difference between a correlational design and an experimental design?

Answer 8

Correlational - relationship
Experimental - difference

Question 9

What is a population?

Answer 9

All individuals/ items in your inclusion criteria.

Question 10

What is a Sample?

Answer 10

A subset of the population.

Question 11

What is a sampling error?

Answer 11

The difference between the values of the sample statistic and the population parameter.

Question 12

How do you minimise the sample error?

Answer 12

By choosing a sample as representative of the population as possible.

Question 13

What is falsification?

Answer 13

Hypotheses must make clear predictions that can be tested and proved false.

Question 14

What H do we assume is true?

Answer 14

H0 e.g. coffee consumption is not related to alertness.

Question 15

Which statement do we assume is true?
Coffee consumption is positively related to alertness. OR Coffee consumption is not related to alertness.

Answer 15

Coffee consumption is not related to alertness. H0 - Null hypothesis.

Question 16

What does the p-value test?

Answer 16

The probability of the null hypothesis being true. Measures how likely it is that any observed difference between groups is due to chance.

Question 17

What is the p-value?

Answer 17

Defined as the probability under the assumption of no effect or no difference (null hypothesis), of obtaining a result equal to or more extreme than what was actually observed.

Question 18

What would it look like if the p-value is considered small?

Answer 18

<5% - 0.05.

Question 19

What does it mean if the p-value is less than 5% - 0.05?

Answer 19

We can reject the null hypothesis.

Question 20

What does the p-value tell us and what does it not tell us?

Answer 20

Tells us the alternative hypothesis is more likely to be correct.
Doesn’t tell us that the null is true but your sample was unsual or that the null is false.

Question 21

What is a type 1 error?

Answer 21

A false positive.

Question 22

What is a type 2 error?

Answer 22

A false negative.

Question 23

What happens if the data does not contradict H0?

Answer 23

• we conclude that the observed data set provides no evidence against the null hypothesis.

Question 24

What happens is the H0 is accepted?

Answer 24

• meaning that the data give insufficient evidence to make any conclusion.
• meaning there is no evidence to support changing from a currently used explanation of a phenomenon.

Question 25

What is Confidence Interval (CI)

Answer 25

Is a range of values that’s likely to include the true population mean with a certain degree of confidence. Often expressed as a % whereby a population mean lies between between an upper and lower interval.

Question 26

What is effect size?

Answer 26

• a quantitative measure of the magnitude of the experimental effect.

Question 27

What does effect size mean on variables?

Answer 27

The larger the effect size, the stronger the relationship between two variables.

Question 28

Where is effect size widely used?

Answer 28

In meta-analysis.

Question 29

How to report p-values?

Answer 29

Report exact values to two or three decimal places.
Report p values less than .001 as p<.001.
P-values >.0.5 can report p>.05.

Question 30

Reporting confidence intervals

Answer 30

95% CI [lower limit, upper limit]