Our Master Statistics course takes you from the basics to statistical inference: descriptive statistics, probability, discrete and continuous distributions, sampling, confidence intervals, hypothesis tests (t-tests, chi-square and ANOVA), correlation and regression. Lessons are live and one-to-one with a verified statistics tutor, who explains when to use each test and how to interpret output in SPSS, R or Excel. Suitable for university students in psychology, business, nursing, social sciences and STEM.
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$14 – $36
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What you will learn
Prerequisites
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In short: this course gives one-to-one online tutoring in statistics for university students. A verified tutor explains descriptive statistics, probability, distributions, hypothesis testing, regression and software output in Excel, SPSS or R.
Statistics is the science of collecting, summarising and analysing data to draw conclusions under uncertainty. A statistics course covers describing data with averages and spread, probability and distributions, and inferential methods such as confidence intervals, hypothesis tests and regression, used in research across science, business, health and social science.
This online course teaches Statistics one to one with a verified tutor who has studied or taught it at university level, at your pace and aligned with your own module.
Related subject: Statistics help.
For the theory behind these methods, see our Probability Theory course.
| Topic | Key content |
|---|---|
| Fundamentals | Types of data, variables, scales of measurement, population and sample, descriptive versus inferential statistics |
| Descriptive statistics | Mean, median, mode, range, variance, standard deviation, percentiles, interquartile range |
| Probability | Probability rules, conditional probability, binomial and normal distributions |
| Hypothesis testing | Null and alternative hypotheses, p-values, significance levels, type I and II errors, confidence intervals |
| Statistical tests | One-sample, paired and independent t-tests, ANOVA, chi-square, Pearson and Spearman correlation, linear regression |
| Question | Data | Test |
|---|---|---|
| Compare one mean with a value | One numeric sample | One-sample t-test |
| Compare two related means | Paired numeric data | Paired t-test |
| Compare two independent means | Two groups, numeric | Independent t-test |
| Compare three or more means | Groups, numeric | One-way ANOVA |
| Association between two categories | Counts | Chi-square test |
| Linear relationship | Two numeric variables | Pearson correlation or regression |
| Relationship with ranks or non-normal data | Ordinal or skewed | Spearman correlation |
Here is a typical question: a sample of 16 has mean 52 and standard deviation 4. Test whether the population mean differs from 50 at the 5% level.
| Step | Working |
|---|---|
| Hypotheses | H₀: μ = 50; H₁: μ ≠ 50 |
| Test statistic | t = (52 − 50) ÷ (4 ÷ √16) = 2 ÷ 1 = 2.0 |
| Degrees of freedom | 16 − 1 = 15 |
| Critical value (two-tailed, 5%) | 2.131 |
| Decision | 2.0 < 2.131, so do not reject H₀ |
Students most often lose marks in Statistics through the habits below. Each is easy to fix with practice and feedback on your own work.
| Mark-loser | What to do instead |
|---|---|
| Wrong test chosen | Match the test to the data and question |
| Assumptions unchecked | Check normality and independence |
| p-values misread | A p-value is not the probability H₀ is true |
| Conclusions without context | State the result in the question's context |
| Software output pasted without explanation | Interpret every number you report |
Statistics modules are usually assessed by exams with calculations and interpretation, plus coursework analysing data in software such as SPSS, R, Excel or Python.
Tutors explain concepts, work through practice problems with you and give feedback on your approach. Graded work you submit must be your own, as set out in our academic integrity policy. For deadline-driven guidance, see assignment help.
Lessons are live and one to one on video with screen sharing and a shared whiteboard. You can follow a full course or book sessions on the topics you find hardest before exams. See also Probability Theory, Python for Data Science and our Biostatistics subject page.
A statistics tutor online gives you live 1-to-1 lessons on the methods your course uses, from descriptive statistics to hypothesis tests and regression, and helps you choose the right test and interpret the result.
This course suits university students taking a statistics or research methods module in any subject, students analysing data for a dissertation, and professionals who need statistics at work.
Prerequisites: basic algebra, percentages and reading graphs. No previous statistics is needed.
Course length: 40 one-hour lessons across 5 modules.
| Stage | Modules | What you can do afterwards |
|---|---|---|
| 1 | Statistics fundamentals, descriptive statistics | Summarise data with averages, spread and charts |
| 2 | Probability | Work with distributions, including the normal distribution |
| 3 | Hypothesis testing | Set hypotheses, find p-values and draw correct conclusions |
| 4 | Statistical tests | Run and interpret t-tests, ANOVA, chi-square, correlation and regression |
Where it leads: continue with our Python for Data Science course or Machine Learning.
The science of collecting, summarising and analysing data to draw conclusions under uncertainty.
The probability of results at least as extreme as those observed if the null hypothesis is true.
A t-test compares two means; ANOVA compares three or more.
Correlation measures the strength of a relationship; regression models how one variable predicts another.
SPSS, R, Excel or Python, depending on your course.
Basic algebra is enough to start; tutors explain the concepts behind the formulas.
Tutors explain which tests suit your data and how to interpret results; the analysis you submit must be your own.
Yes. Tutors practise calculations and interpretation.
The price depends on the tutor and session length. You see the hourly rate before booking, with no subscription.
No. Tutors explain, guide and give feedback so you can complete your own work, in line with our academic integrity policy.
t = (sample mean − hypothesised mean) ÷ (s ÷ √n).
n − 1.
A z-test uses a known population standard deviation; a t-test estimates it from the sample.
A range likely to contain the population parameter at a chosen confidence level.
A model of how an outcome changes with one or more predictors.
Sample means are approximately normal for large samples, whatever the population shape.
A statistics course teaches you to collect, summarise and analyse data and draw conclusions from it. It usually starts with descriptive statistics and probability, then moves to hypothesis testing, confidence intervals and regression. Many courses also use software such as Excel, SPSS, R or Python to apply the methods to real data.
Statistics feels hard for many students because it mixes calculation with interpretation: you must know which test to use and what the result means. Hypothesis testing, p-values, confidence intervals and regression usually need the most practice. It becomes manageable when you work through real datasets step by step, and your tutor focuses there.
Yes. Lessons are online, so you can book times that suit your time zone.
Statistics skills are used in data analysis, data science, market research, finance and insurance, public health, government and academic research. Roles include data analyst, statistician and research analyst, and some, such as actuary, need further professional exams. Even outside these roles, statistics helps you make decisions based on evidence.
You can find a statistics tutor online through FLN Tutors, with live 1-to-1 video lessons built around your own course. Your tutor helps you choose the right test, works through calculations step by step, and can explain output from software such as SPSS, R or Excel, so you can interpret results yourself.
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