Subject · GCSE
Statistics
Which schools in England make Statistics a big part of the curriculum, and where results are strongest. Entries and grades from the 2025 performance tables.
What the data says
Statistics GCSE was taken in 1,064 schools in England in 2025, with 31,919 entries — about 5% of the Year 11 cohort.
Nationally 20% of entries achieved grade 7 or above. The tables below show the schools where the largest share of pupils take the subject, and where grades are highest (schools with at least 20 entries).
At Aylesbury Grammar School (Buckinghamshire) 100% of the year group took Statistics.
GCSE: schools with the highest share of grades 7–9
Schools with at least 20 entries. England: 20% of entries graded 7–9. High grades often reflect who is admitted (selective and independent schools) as much as teaching.
| # | School | Local authority | Entries | Cohort | Share | Grades 7–9 |
|---|---|---|---|---|---|---|
| 1 | King Edward VI Grammar School, Chelmsford | Essex | 111 | 150 | 74% | 100% |
| 2 | Tanbridge House School | West Sussex | 27 | 298 | 9% | 100% |
| 3 | Blackheath High School GDST | Greenwich | 24 | 88 | 27% | 100% |
| 4 | Newstead Wood School | Bromley | 21 | 156 | 13% | 100% |
| 5 | Brampton Manor Academy | Newham | 20 | 361 | 6% | 100% |
| 6 | King Edward VI School | Warwickshire | 30 | 90 | 33% | 97% |
| 7 | Woodbridge School | Suffolk | 24 | 72 | 33% | 96% |
| 8 | St Edmund's Catholic School | Portsmouth | 23 | 223 | 10% | 96% |
| 9 | Reigate Grammar School | Surrey | 22 | 163 | 13% | 95% |
| 10 | Concord College | Shropshire | 55 | 103 | 53% | 95% |
| 11 | The Beaulieu Park School | Essex | 35 | 179 | 20% | 94% |
| 12 | Bohunt School | Hampshire | 32 | 304 | 11% | 94% |
| 13 | Lawrence Sheriff School | Warwickshire | 46 | 150 | 31% | 91% |
| 14 | Queen Ethelburga's Prep School | North Yorkshire | 20 | 48 | 42% | 90% |
| 15 | Downsend School | Surrey | 20 | 73 | 27% | 90% |
| 16 | Sandbach School | Cheshire East | 20 | 232 | 9% | 90% |
| 17 | Tauheedul Islam Girls' High School | Blackburn with Darwen | 29 | 124 | 23% | 90% |
| 18 | Aylesbury Grammar School | Buckinghamshire | 186 | 186 | 100% | 89% |
| 19 | Richard Lander School | Cornwall | 50 | 280 | 18% | 88% |
| 20 | St Bernard's Catholic Grammar School | Slough | 78 | 158 | 49% | 87% |
| 21 | Robert Clack School | Barking and Dagenham | 31 | 466 | 7% | 87% |
| 22 | Davison Church of England High School for Girls, Worthing | West Sussex | 31 | 258 | 12% | 87% |
| 23 | The Downs School | West Berkshire | 23 | 206 | 11% | 87% |
| 24 | Weydon School | Surrey | 29 | 320 | 9% | 86% |
| 25 | Newquay Tretherras | Cornwall | 21 | 280 | 8% | 86% |
| 26 | Harris Academy Sutton | Sutton | 31 | 205 | 15% | 84% |
| 27 | Wellington School | Trafford | 37 | 259 | 14% | 84% |
| 28 | Wembley High Technology College | Brent | 43 | 306 | 14% | 84% |
| 29 | Haberdashers' Hatcham College | Lewisham | 29 | 223 | 13% | 83% |
| 30 | Kings Priory School | North Tyneside | 29 | 124 | 23% | 83% |
| 31 | Woking High School | Surrey | 57 | 233 | 24% | 82% |
| 32 | Bourne Grammar School | Lincolnshire | 89 | 239 | 37% | 82% |
| 33 | Beauchamp College | Leicestershire | 38 | 294 | 13% | 82% |
| 34 | The Petersfield School | Hampshire | 53 | 255 | 21% | 81% |
| 35 | Tunbridge Wells Grammar School for Boys | Kent | 21 | 217 | 10% | 81% |
| 36 | Culford School | Suffolk | 21 | 108 | 19% | 81% |
| 37 | Birchwood High School | Hertfordshire | 21 | 243 | 9% | 81% |
| 38 | Park Hall Academy | Solihull | 26 | 199 | 13% | 81% |
| 39 | Stour Valley Community School | Suffolk | 26 | 113 | 23% | 81% |
| 40 | Wildern School | Hampshire | 62 | 427 | 15% | 81% |
What pupils learn in Statistics, stage by stage
England has no national year-by-year syllabus: exam boards and schools set the order within each stage. These are the statutory blocks from the Department for Education, so a parent can see what a child meets in Years 7–9, what every GCSE candidate must cover, and what waits in the sixth form.
Years 7–9Key Stage 3
not offered as a separate subjectYears 10–11GCSE
119learning outcomes · statutory GCSE subject content; Higher-tier-only outcomes are markedYear 12A level, typically first year
not offered as a separate subjectYear 13A level, second year
not offered as a separate subjectWhat GCSE Statistics teaches · Years 10–11
119 learning outcomes from the Department for Education’s statutory subject content, grouped by topic. Each outcome belongs to one of twelve fields of knowledge used across startfrom.today: Mathematics & Statistics (119). Exam boards add detail on top of these; the outcomes themselves are the same whichever board a school uses.
A: Planning a statistical enquiry3
- test a hypothesis through appropriate collection and analysis of data — data analysis, data collection, hypothesis
- identify constraints in designing a statistical investigation — constraint, cost, ethical issue, time
- determine strategies to mitigate issues arising during a statistical enquiry — mitigation strategy, non-response, statistical enquiry
B: Collecting and sourcing data28
- classify data as categorical, ordinal, discrete or continuous — categorical data, continuous data, discrete data, ordinal data
- classify data as quantitative or qualitative — qualitative data, quantitative data
- classify data as ungrouped or grouped — grouped data, ungrouped data
- identify bivariate data sets — bivariate data
- identify multivariate data setsHigher tier — multivariate data
- group numerical data into class intervals — class interval, grouped data
- distinguish explanatory and response variables — explanatory variable, response variable
- distinguish between primary and secondary data — primary data, secondary data
- identify sources of data including experiments, questionnaires and census — census, data source, experiment, questionnaire
- acknowledge sources of secondary data — citation, secondary data
- assess the reliability and validity of collected data — reliability, validity
- identify factors that lead to bias in data collection — bias, sensitivity
- apply techniques to minimise data distortion — bias mitigation, data distortion
- evaluate the level of control in an investigation designHigher tier — investigation design, level of control
- distinguish between population, sample frame and sample — population, sample, sample frame
- evaluate risks of bias in judgement and convenience sampling — bias, convenience sampling, judgement sampling
- apply random and systematic sampling techniques to avoid bias — random sampling, systematic sampling
- apply quota sampling to avoid bias — quota sampling
- describe key features of a simple random sample — simple random sample
- select random sample members using physical or electronic techniques — random selection, sampling technique
- apply stratification before sampling takes place — stratified sampling
- design data collection questions that avoid leading or biased wording — bias, leading question, questionnaire design
- distinguish between open and closed questions — closed question, open question
- select an appropriate interview technique for data collection — interview technique
- apply techniques to handle missing, incomplete or incorrectly formatted data — data format, incomplete response, missing data
- clean data to prepare it for further processing — data cleaning
- identify and control extraneous variables in an investigation — extraneous variable
- apply control groups in an experimental designHigher tier — control group
C: Data visualisation14
- represent data using tabulation, tally charts and pictograms — pictogram, tabulation, tally chart
- interpret and compare data displayed as pie charts, stem-and-leaf or Venn diagrams — pie chart, stem-and-leaf diagram, venn diagram
- interpret and compare data displayed as population pyramids or choropleth maps — choropleth map, population pyramid
- interpret comparative pie charts and 2D or 3D representationsHigher tier — 2d representation, 3d representation, comparative pie chart
- represent and interpret data using bar charts, line charts and scatter charts — bar chart, line chart, scatter chart, time series
- interpret cumulative frequency charts, frequency polygons and box plots — box plot, cumulative frequency, frequency polygon
- draw histograms with equal class width — equal class width, histogram
- calculate frequency density to draw histograms with unequal class widthHigher tier — frequency density, unequal class width
- justify the choice of format for a data visualisation — data visualisation, format selection
- recognise errors in graph construction that cause misrepresentation — graphical misrepresentation, scale, truncated axis
- recognise misuse of the frequency density formula in histogramsHigher tier — frequency density, misrepresentation
- extract and calculate values to compare data sets in different formats — data comparison, format conversion
- present the same data in multiple formats — data representation
- select and justify an appropriate form of representation for the data — data type, representation choice
D: Statistical measures17
- calculate the mode, median and arithmetic mean for discrete and grouped data — arithmetic mean, median, mode
- calculate the weighted mean, geometric mean and mean seasonal variationHigher tier — geometric mean, seasonal variation, weighted mean
- justify the choice of average for a given context — average, rationale
- determine skewness from data by inspection — skewness
- calculate skewness using a given formulaHigher tier — formula, skewness
- calculate the range and quartiles for a data set — quartile, range
- calculate the interquartile range and percentiles — interquartile range, percentile
- calculate the interpercentile range, interdecile range and standard deviationHigher tier — interdecile range, interpercentile range, standard deviation
- identify outliers in a data set by inspection — outlier
- identify outliers using calculated thresholdsHigher tier — outlier, threshold calculation
- identify trends in data by inspection — trend
- calculate moving averages to identify trends in data — moving average, trend
- draw a line of best fit by eye — line of best fit
- draw a line of best fit through a calculated double mean point — double mean point, line of best fit
- determine the equation of a regression lineHigher tier — equation, regression line
- calculate Spearman's rank correlation coefficientHigher tier — rank correlation coefficient
- calculate estimates of probability from collected data — probability estimation
E: Interpreting and evaluating results39
- compare probabilities of outcomes using a 0-1 or percentage scale — probability scale
- calculate the expected frequency of a characteristic in a sample or population — expected frequency
- interpret relative and absolute risk from collected data and probabilities — absolute risk, relative risk
- compare experimental data with theoretical predictions to identify bias — bias, experimental probability, theoretical probability
- recognise that experimental probability tends towards theoretical probability — experimental probability, theoretical probability
- represent outcomes of up to three events using two-way tables or tree diagrams — tree diagram, two-way table, venn diagram
- compare data sets using measures of central tendency — mean, median, mode
- compare data sets using the range — range
- compare data sets using interquartile range or percentiles — interquartile range, percentile
- compare data sets using standard deviationHigher tier — standard deviation
- compare sample data with population data using median and interquartile range — population data, sample data
- apply formal notation for independent events — independent events, notation
- apply formal notation for conditional probability — conditional probability, notation
- interpret a data distribution in terms of skewness — distribution, skewness
- comment on outliers with reference to the original data — outlier
- interpret seasonal and cyclic trends in context — cyclic trend, seasonal trend
- use trends to make predictionsHigher tier — prediction, trend
- interpret rates of change over time from graphical data — graphical data, rate of change
- calculate rates of change over time from tabulated data — rate of change, table
- calculate and interpret index numbers such as retail or consumer price index — index number, retail price index
- calculate weighted index numbers in contextHigher tier — weighted index number
- classify correlation as positive, negative or zero — correlation, negative correlation, positive correlation
- use interpolation and extrapolation to estimate values from correlated data — extrapolation, interpolation
- compare the strength of correlation by inspection — correlation strength
- distinguish correlation from causation — causation, correlation
- recognise that multiple factors may interact to affect correlationHigher tier — confounding factor, correlation
- interpret a given Spearman's rank correlation coefficient in context — rank correlation coefficient
- interpret a given Pearson's product moment correlation coefficient in contextHigher tier — product moment correlation coefficient
- distinguish Spearman's rank correlation from Pearson's product moment correlationHigher tier — product moment correlation, rank correlation
- comment on differences between experimental and theoretical values in terms of biasHigher tier — bias, experimental value, theoretical value
- interpret the characteristics of a binomial distributionHigher tier — binomial distribution
- interpret the characteristics of a Normal distributionHigher tier — normal distribution
- apply the empirical rule to interpret standard deviations from the meanHigher tier — normal distribution, standard deviation
- use action and warning lines in quality assurance samplingHigher tier — control chart, quality assurance
- standardise and compare data from two samples using mean and standard deviationHigher tier — mean, standard deviation, standardisation
- use sample data to estimate population mean and proportions — population mean, population proportion, sample data
- apply the Petersen capture-recapture formula to estimate population sizeHigher tier — capture-recapture, population size
- explain the impact of sample size on reliability and replication — reliability, replication, sample size
- recognise that sample means are more closely distributed than individual valuesHigher tier — sample mean, sampling distribution
Appendix 3: Statistical enquiry cycle18
- define a question or hypothesis to investigate — hypothesis, research question
- decide what data to collect and how to record it, giving reasons — data collection plan
- develop a strategy for processing and representing data, giving reasons — data processing strategy
- recognise constraints in designing primary data collection methods — constraint, primary data
- research secondary data sources including publications and the media — secondary data, source
- acknowledge sources of data used in an investigation — citation, data source
- recognise how sensitivity issues may influence data availability — data availability, sensitivity
- organise and process data using appropriate technology — data processing, technology
- generate diagrams and visualisations to represent data using technology — data visualisation, technology
- generate statistical measures to compare data using technology — statistical measure, technology
- analyse diagrams and calculated measures in context — data interpretation
- reach conclusions that relate to the original question or hypothesis — conclusion, hypothesis
- make inferences and predictions from statistical findings — inference, prediction
- discuss the reliability of statistical findings — findings, reliability
- communicate findings clearly and concisely for a target audience — communication, target audience
- identify weaknesses in a statistical approach or representation — evaluation, weakness
- suggest improvements to a statistical process or presentation — evaluation, improvement
- refine a statistical process to clarify the initial hypothesis — hypothesis, refinement
Source: DfE GCSE statistics subject content (2016), Open Government Licence v3.0. Outcomes are split into single actions and rewritten only where the original bundled several; wording otherwise follows the DfE text. Methodology.