What descriptive statistics are examinable in Advanced Higher Geography?
Descriptive statistics: measures of central tendency (mean, median, mode) and measures of dispersion (range, interquartile range, standard deviation, standard error of the mean, coefficient of variation).
The examinable descriptive statistics in SQA Advanced Higher Geography: measures of central tendency (mean, median, mode) and measures of dispersion (range, interquartile range, standard deviation, standard error of the mean, coefficient of variation), and what each reveals about a data set.
Reviewed by: AI editorial process; not yet individually human-reviewed
Have a quick question? Jump to the Q&A page
Jump to a section
What this key area is asking
Descriptive statistics summarise a data set. The spec splits them into measures of central tendency (mean, median, mode) and measures of dispersion (range, interquartile range, standard deviation, standard error of the mean, coefficient of variation). You should know what each measure is, what it reveals, and why both an average and a measure of spread are needed to describe data fully.
Measures of central tendency
Each average has strengths. The mean is the standard summary for symmetric interval data; the median is safer when the data is skewed or has outliers; the mode is the only average for nominal data.
- Mean. Sum divided by count; uses all data; sensitive to outliers.
- Median. Middle value when ranked; robust to outliers.
- Mode. Most frequent value; the only average for nominal data.
Measures of dispersion
Spread matters as much as the average. The range (highest minus lowest) is simple but outlier-sensitive; the interquartile range (the middle 50%) resists outliers; the standard deviation uses every value; the standard error of the mean judges how reliable the sample mean is; and the coefficient of variation compares relative variability fairly.
A routine for describing a data set
- Choose an average. Mean for symmetric data; median for skewed; mode for categorical.
- Choose a spread measure. Standard deviation with the mean; interquartile range with the median.
- Interpret both. Say what the average and the spread reveal together.
- Compare fairly. Use the coefficient of variation to compare data sets with different means.
Examples in context
Try this
Q1. Name the three measures of central tendency. [3 marks]
- Cue. Mean, median and mode.
Q2. What does the coefficient of variation allow you to do that the standard deviation alone does not? [1 mark]
- Cue. Compare the relative variability of data sets fairly even when their means or units differ.
Exam-style practice questions
Practice questions written in the style of SQA exam questions on this dot point, with worked answer explainers. The year tag is the paper they imitate, not the source.
SQA AH data4 marksExplain the difference between measures of central tendency and measures of dispersion, naming examples of each.Show worked answer →
Measures of central tendency describe the typical or central value of a data set: the mean (the arithmetic average), the median (the middle value when ranked) and the mode (the most frequent value). Measures of dispersion describe how spread out the data is: the range, the interquartile range, the standard deviation, the standard error of the mean and the coefficient of variation.
A full answer defines the two groups, names examples of each, and explains why both are needed: two data sets can share a mean but differ greatly in spread, so a measure of dispersion is needed alongside an average to describe the data fully. The strongest answers note that the median and interquartile range resist outliers, while the mean and standard deviation use every value.
SQA AH data5 marksExplain what the standard deviation and the coefficient of variation each tell you about a data set.Show worked answer →
The standard deviation measures the average distance of values from the mean, in the same units as the data, so a larger standard deviation means a more spread-out data set. The coefficient of variation expresses the standard deviation as a percentage of the mean, so it compares the relative variability of data sets with different means or units.
Strong answers define each, explain what a large or small value means, and give the key use of the coefficient of variation: comparing the spread of two data sets fairly even when their means differ (for example rainfall variability between a wet and a dry station). They may add that the standard error of the mean indicates how reliable the sample mean is as an estimate of the true mean.
Related dot points
- Handling data types and sampling: distinguishing nominal, ordinal and interval data, and choosing random, regular or stratified sampling, so that the right presentation and statistical test can be selected.
How to handle data types and sampling in SQA Advanced Higher Geography data handling: distinguishing nominal, ordinal and interval data and choosing random, regular or stratified sampling, so the correct graph and statistical test can be selected for the data.
- Graphical presentation of data: bipolar analysis, dispersion diagram, kite diagram, logarithmic graph, polar graph, systems diagrams, scattergraph and triangular graph, and choosing the right graph for the data.
The examinable graphical techniques in SQA Advanced Higher Geography: bipolar analysis, dispersion diagram, kite diagram, logarithmic graph, polar graph, systems diagrams, scattergraph and triangular graph. Covers what each shows and how to choose the right graph for the data.
- Mapping and map-based diagrams: annotated overlay, choropleth map, cross section, dot map, flow line map, isoline map, proportional symbols, sphere of influence map and transect, and choosing the right one for the data.
The examinable mapping and map-based diagram techniques in SQA Advanced Higher Geography: annotated overlay, choropleth, cross section, dot map, flow line, isoline, proportional symbols, sphere of influence and transect. Covers what each shows and how to choose the right one.
- Correlation tests: Spearman's rank correlation for ranked data and Pearson's product moment correlation coefficient for interval data, interpreting the coefficient and its significance.
How to use the two correlation tests in SQA Advanced Higher Geography: Spearman's rank correlation coefficient for ranked data and Pearson's product moment correlation coefficient for interval data, including interpreting the coefficient between minus 1 and plus 1 and judging significance.
- Evaluating fieldwork techniques: judging the reliability, accuracy and limitations of a method and its data, identifying sources of error and bias, and suggesting improvements.
How to analyse and evaluate fieldwork techniques in SQA Advanced Higher Geography: judging the reliability, accuracy and limitations of a method and its data, identifying sources of error and bias, and suggesting improvements to strengthen an investigation.
Sources & how we know this
- Advanced Higher Geography Course Specification — SQA (2019)
- Advanced Higher Geography Specimen Question Paper — SQA (2019)