Qualification: A-Level | Exam board: Edexcel
A-LEVEL STATISTICS PROVIDES STUDENTS WITH A WIDE RANGE OF TECHNIQUES FOR UNDERSTANDING AND ANALYSING DATA, WHICH ARE INCREASINGLY IMPORTANT SKILLS IN A WORLD OF CONSTANT CHANGE.
The emphasis of the qualification is on understanding the implications and limitations of the various techniques in the context of practical situations. The course provides useful support for a variety of other subjects, including many of the Social Sciences.
This course will cover a range of topics including:
- data handling
- correlation and regression
- probability distributions and approximations
- hypothesis testing.
Year One content
1. Numerical measures, graphs and diagrams
Students will interpret statistical diagrams including bar charts, stem and leaf diagrams, box and whisker plots, cumulative frequency diagrams, histograms (with either equal or unequal class intervals), time series and scatter diagrams.
Students will learn how to:
- Know and use language and symbols associated with set theory in the context of probability
- Represent and interpret probabilities using tree diagrams, Venn diagrams and two-way tables
- Calculate and compare probabilities: single, independent, mutually exclusive and conditional probabilities
- Use and apply the laws of probability to include conditional probability
- Include addition and multiplication laws
- Determine if two events are statistically independent
3. Population and samples
- Students will evaluate the practical application of random and non-random sampling techniques such as simple random, systematic, cluster, judgmental and snowball. You will also include the use of stratification (in proportional and disproportional ratios) prior to sampling taking place.
4. Introduction to probability distributions
Students will learn how to:
- Know and use terms such as: random, discrete, continuous, dependent, and independent for variability (with reference to random variables).
Calculate probabilities and determine expected values, variances and standard deviations for discrete distributions.
How to explain in words, such distributions given in a table or by definition of the discrete probability function.
Use discrete random variables to model real-world situations.
Know the properties of a continuous distribution
5. Binomial distribution
- Know when a binomial model is appropriate (in real world situations including modelling assumptions).
- Know methods to evaluate or read probabilities using formula and tables.
- Use calculator functions to obtain binomial probabilities and are advised to do so.
- Calculate and interpret the mean and variance
6. Normal distribution
- Understand the specific properties of the normal distribution, and that data from such an underlying population would approximate to having these properties, with different samples showing variation
7. Correlation and linear regression
- Calculate (only using appropriate technology ‒ calculator) and interpret association using Spearman’s rank correlation coefficient or Pearson’s product moment correlation coefficient.
8. Introduction to hypothesis testing
- Use and demonstrate understanding of the terms parameter, statistic, unbiased and standard error.
9. Contingency tables
- Construct contingency tables from real data, combining data where appropriate, and interpret results in context.
10. One and two sample non-parametric tests
- Use sign or Wilcoxon signed-rank tests to investigate population median in single sample tests and also to investigate for differences using a paired model.
Year Two content
1. Bayes’ theorem
- Calculate and use conditional probabilities to include Bayes’ theorem for up to three events, including the use of tree diagrams.
2. Probability distributions
- Understand the use and validity of distributions which could be appropriate in a particular real world situations. This includes binomial, normal, Poisson and exponential distributions.
3. Experimental design
- Understand, know and discuss issues involved in experimental design including experimental error, randomisation, replication, control and experimental groups, and blind and double blind trials.
4. Sampling, estimates and resampling
- Use and demonstrate the understanding of terms such as parameter, statistic, unbiased and standard error.
5. Hypothesis testing, significance testing, confidence intervals and power
- Use confidence intervals for the mean using z or t as appropriate and interpret results in practical contexts.
6. Paired tests
- Use sign, Wilcoxon signed-rank or paired t-test, understanding appropriate test selection and interpreting the results in context.
7. Exponential and Poisson distributions
- Learn how to determine when a Poisson model is appropriate (in real world situations including modelling assumptions).
8. Goodness of fit
- Conduct a statistical ‘goodness of fit test’ for binomial, Poisson, normal and exponential distributions or a specified discrete distribution.
9. Analysis of variance
- Conduct one-way analysis of variance, using a completely randomised design with appreciation of the underlying model with additive effects and experimental errors .
10. Effect size
- Know the notion of effect size as a complementary methodology to standard significance testing, and apply in authentic contexts.
Studying a subject with real-world applications allows students to:
- Understand the Statistics they see in the world around them and knowing where they are useful, and their limitations
- Develop models, testing them and then refining and improving them
- Be able to use their Statistics knowledge in other fields of study
- Learn techniques that also apply to other subjects, at A-Level and beyond
- Build practical and analytical skills that will help them in many different careers
- Develop the fundamental statistical and lateral thinking skills that are invaluable across all kinds of disciplines and careers.
A-Level Statistics is a good choice for students considering Higher Education in any Science, Social Science or Maths-based course or Medicine, Law or Management. Career opportunities for students who study A-Level Statistics include: industry, management, healthcare, accountancy, finance, economics, insurance, medicine, law, social work, communications, veterinary science and engineering
You will be taught in a small group and will be encouraged to meet at other times during the week in a study group to support each other. There are also times during the week when you can access staff for support where necessary.
Students will be assessed internally on a regular basis, including through formal mock exams. There are final exams at the end of the course in Year two
The A-Level assessment contain 3 written papers:
- Paper One: Data & Probability
- Paper Two: Statistical Inference
- Paper Three: Statistic in Practice
The course encourage independent learning, time management, working in small groups providing support for each other and, of course, a deep love of the subject.
Some of our subjects are subject to standard entry requirement and some have additional requirements. Please see below:
Subject specific entry requirements
- 6 in Maths
Standard entry requirements
- At least five GCSEs at grades 9-4 including English Language and Maths (one Vocational or Technical qualification at Merit or above may be counted)
- At least grade 4 (or Merit for Vocational or Technical qualifications) in any subject(s) that you have taken at GCSE and wish to continue studying at Level 3
- Average GCSE (best 8) of at least 4 (38 on the old points table)
- Grade 4 (or Merit), at least, in any subject previously studied
- Students will take either 3 A-Level subjects or 1 Diploma subject with 1 A-Level