Descriptive statistics

Question 1

What are descriptive and inferential statistics?

Answer 1

Descriptive stats = summarise the midpoint and spread in data
Inferential stats = identify significant differences or relationships

Question 2

What are the measures of central tendency?

Answer 2

They summarise a data set with one single number
Mode, median and mean

Question 3

Mode - what does it measure, what data should it be used for, strengths and limitations

Answer 3

Most frequently occurring score in a data set
Nominal data
:) - Simple to explain and easy to calculate
:( - Can have more than one value: bimodal or multimodal
:( - Can fluctuate across samples as it is taken from a single data point

Question 4

Median - what does it measure, what data should it be used for, strengths and limitations

Answer 4

Middle score when data points arranged from smallest to largest
Ordinal data
:) - Unaffected by outlying scores and skewed distributions
:( - Can fluctuate across samples as it is taken from a single data point

Question 5

Mean - what does it measure, what data should it be used for, strengths and limitations

Answer 5

Sum of scores, divide by number of scores
Interval or ratio data
:) - Calculated from all data points so it is resistant to differences across samples
:( - Very influenced by any extreme or outlying scores as it is calculated from every data point

Question 6

What are measures of dispersion?

Answer 6

These measure how consistent the points in a data set are
Range, variance and standard deviation

Question 7

What is the range and interquartile range?

Answer 7

Range = difference between smallest and largest data point
Interquartile range (IQR) = range ignoring the smallest and largest 25%
- Scores at each quartile - 25% (Q1), 50% (Q2) and 75% (Q3)
- Calculate interquartile range = Q3 - Q1

Question 8

What is variance? Between groups and within groups variance?

Answer 8

Variance = how widely distributed the points are around the mean
Between groups variance (experimental variance) = variation between means of different experimental conditions
- Want this to be big
Within groups variance (random variance) = variation within each experimental group
- Want this to be small

Question 9

What is standard deviation?

Answer 9

Square root of variance
How widely distributed points are around the mean

Question 10

What is the sample?

Answer 10

All participants you collect data from in your study

Question 11

What is the population?

Answer 11

All the possible people that could have been included in your study

Question 12

What is a null hypothesis?

Answer 12

Null hypothesis (H0) - experimental manipulation will have no effect (no significant difference)

Question 13

What is an alternative hypothesis? (One-tailed and two-tailed)

Answer 13

Alternative hypothesis (H1) - prediction that effects will be found
One tailed - the direction of predicted effects is specified
Two-tailed - prediction that effects will be found but without directional predictions

Question 14

What is a type 1 error?

Answer 14

False positive
There is no real effect in population but an effect is found in the sample (e.g. 10% significance)
Reject null when you should accept

Question 15

What is a type 2 error?

Answer 15

False negative
There is a real effect in the population that you fail to find in your sample (e.g. 1% significance)
Accept null when you should reject

Question 16

What is a p value and when is a finding significant?

Answer 16

P value = probability of having committed a type 1 error
E.g. 5% chance of type 1 error
p≤ 0.05 = significant findings
p≥ 0.05 = not significant findings
More variance between than within groups = significant