1. Descriptive Statistics

2. Descriptive Statistics
Summarising, organising and describing raw data
Limitation: can’t tell whether the difference is large enough to attribute to the IV, or whether it has occurred by chance
Does not establish a cause-effect relationship between the variables

3. Includes: Percentages Measures of central tendency Spread of scores
Graphs

4. 1. Percentages The proportion of a sample
Quick, effective comparison tool
Number of people with feature X 100
size of the sample

5. Question 1
Of 12 subjects, 2 scored 2 points, 5 scored 4 points, 4 scored 3 points and 1 scored 9 points.
Compare the percentage of the sample who scored 4 points with that who scored 2 points.

6. 2. Measures of Central Tendency
Provides one score that represents all the scores in the sample.
Includes:
Mean
Mode
Median

7. a) Mean
All data scores added together and divided by the number of scores.
Most suitable for interval or ratio data (data on a scale, most precise data)
Strength – most sensitive measure
Weakness – can be distorted by extreme (large or small) scores (outliers)

8. b) Mode The most common score in the set.
Most suitable for nominal data (number of times something has occurred)
Strength – easy to obtain, not influenced by extreme scores
Weakness - Can be unreliable in small samples, not useful if bi/tri modal

9. c) Median
The middle number in the set when they are all arranged in numerical order.
Most suited for measures of ordinal data (data that can be ordered / ranked)
Strength - Not affected by outliers
Weakness – can be distorted by small samples, is less sensitive

10. Question 2.
Of 12 subjects, 2 scored 2 points, 5 scored 4 points, 4 scored 3 points and 1 scored 9 points.
Calculate the mean, median and mode of this sample.
Which measure of central tendency would be best to use in this instance? Explain.

11. 3. Spread of Scores Shows how the data is spread – its variability
Includes:
Range
Standard deviation

12. a) Range
Calculated by subtracting the lowest score from the highest score
Eg: 1,2,3,4,5,6,7,8,9
Range = 9 – 1 = 8
Strength – quick and easy to calculate
Weakness – is directly affected by outliers, may not represent the majority of the data

13. c) Standard Deviation
Calculates the average amount all scores deviate from the mean
A low sd means the data is grouped around the mean, a high sd means the data is spread away from the mean
Strength – most sensitive measure, can be used to relate the sample to the population
Weakness – more time consuming to calculate

14. Example of variance and standard deviation
Eg: Calculate the variance and standard deviation of: 1, 5, 10, 15, 19 Mean = 50/5 = 10 var = (1-10)2 + (5-10)2 +(10-10)2 +(15-10)2 +(19-10)2 /5 = ( ) / 5 = ( ) / 5 = 212/5 = ?? SD = ??

15. Question 4. Calculate the standard deviation of the following data:
1,3,5,7,9

16. 4. Graphing

17. Requirements for graphs
Title that summarises the IV and DV or both axes
Axes labelled with both title and units
Scale – must begin at the origin (0,0) and cuts must be present if required
More than half the graph paper must be used
X-axis is the IV, x-axis is the DV (if applicable)
Points must be plotted as accurately as possible
The correct type of graph for the data must be drawn

18. a) Frequency Distribution Table
Used for large amounts of data
Uses class intervals / categories
Counts how many times a score occurs
Can be graphed as a frequency histogram (intervals on the x axis, frequency on the y axis)
Shows data for all categories, even those with zero frequencies

21. b) Bar Graph
Used for discrete data (categories) that are not on a continuum
Bars must not touch
Categories on the x-axis, frequency on the y-axis

23. c) Pie Chart
Displays categories of data as a proportion of the total population
Data must be converted into % and then converted to degrees of a circle

25. d) Line Graph Shows all data as a series of dots connected by lines
Used for data that is continuous/on a continuum.
Can show more than one data set on the same set of axis.
X-axis for continuous variable. Y-axis what we are measuring.

27. e) Scatter graphs/plot
Plot pairs of scores to show their correlational relationship
Patterns can be calculated using a line of best fit

29. f) Frequency Distribution Curve
Show continuous psychological variables
Bell shaped and symmetrical about the mean
Percentages of scores covered by the curve are known

31. Inferential statistics
Statistical significance
A number obtained from interdental statistics that provides an estimate of how often experimental results could have occurred by chance alone, or by the independent variable.

32. If p is less than 0.05
The difference between control and experimental groups is statistically significant
The difference is unlikely to be due to chance alone
The difference is likely to be due to the independent variable
The hypothesis is supported/a conclusion can be drawn.

33. If p is more than 0.05.
The difference between control and experimental groups is NOT statistically significant
The difference is likely to be due to chance alone
The difference is unlikely to be due to the independent variable
The hypothesis is rejected/no conclusion can be drawn.

35. Revision Questions
1. Put the following into order from lowest to highest probability that the results are due to chance:
2. Circle the values in your list that are statistically significant