Most sport and exercise science dissertations need one of four tests: a paired-samples t test for one group measured twice, a repeated-measures ANOVA for one group measured across several conditions or time points, a mixed ANOVA or ANCOVA for two groups measured before and after an intervention, and Pearson’s r for an association between two performance or physiological measures. The design picks the test; the extra work in this field is reporting reliability and effect size alongside it, because a marker at any BASES-accredited department will ask for both.
This guide is for UK undergraduate and MSc students in sport and exercise science, strength and conditioning, physiotherapy-adjacent sport rehabilitation and sports coaching. It answers the questions students actually bring to a supervisor in the week the data arrive.
Why is the statistics question different in sport science?
Three things separate a sport science dataset from the psychology survey your statistics module was taught on. Samples are small, often eight to fifteen athletes, because testing is physically demanding and time-limited. Designs are mostly within-participant, with each athlete acting as their own control across conditions or sessions. And the outcomes are measurements from equipment (force plates, gas analysers, timing gates, lactate analysers) whose reliability is part of the finding rather than an assumption. The generic decision tree in our guide to choosing a statistical test for a psychology dissertation still applies; what changes is which branches you use most and what you must report around the test.
Is your hypothesis a difference or an association?
Write the hypothesis as a sentence. If it says one condition, group or time point will differ from another on a measure, you need a test of difference. If it says two measures move together across athletes, you need a correlation or regression. Almost every undergraduate sport science project is a test of difference with a repeated-measures element, which is why the rest of this guide leans that way.
Which test for one group measured before and after?
A single group tested at baseline and again after a training block, supplement or warm-up protocol is a paired-samples t test on the change. If the differences are not approximately normal, which is common with eight participants, the Wilcoxon signed-rank test is the fallback, and the honest sentence is that you used it because the difference scores were skewed. Report the mean change with its 95% confidence interval, not just the p value: a marker can read a confidence interval on a change in countermovement-jump height as a finding; a bare p value tells them nothing about size.
Which test for two groups measured before and after?
An intervention group and a control group, both tested pre and post, is the design where most sport science dissertations choose the wrong test. Running two separate paired t tests and declaring that one group improved significantly and the other did not is not a comparison between the groups. The two defensible options are:
- Mixed ANOVA (one between-participant factor, group; one within-participant factor, time), where the finding is the group-by-time interaction, not the main effects.
- ANCOVA on the post-test score with the baseline as a covariate, which Vickers and Altman showed in the BMJ in 2001 is generally the more powerful analysis for controlled designs with baseline and follow-up measurements, and which handles baseline imbalance between small groups better than a change-score comparison.
Either is acceptable at undergraduate level; choose one before you see the data and say why.
Which test for a crossover or several conditions?
The same athletes completing two conditions, caffeine and placebo, cool and hot, compression garment and none, in a counterbalanced order is a crossover. Two conditions on one outcome is a paired-samples t test. Three or more conditions is a one-way repeated-measures ANOVA with post-hoc pairwise comparisons corrected for multiple testing, most often Bonferroni. Two things need a sentence in the methods: how the order was counterbalanced or randomised, and how long the washout was, because an order effect is the most common threat to a crossover and markers look for it.
Which test when you measured at several time points in each condition?
Blood lactate at rest, five, ten and fifteen minutes, in two conditions, is a two-way repeated-measures ANOVA with condition and time as within-participant factors. The finding you want is the condition-by-time interaction. This is also the design in which sphericity fails most often: report Mauchly’s test, apply the Greenhouse–Geisser correction when it is violated, and say so in the results. Where the field is starting to prefer linear mixed models for this design, an undergraduate is not expected to run one, but noting the limitation in the discussion reads well.
How do you report reliability, and why does it carry marks?
In sport science the measurement itself is a result. If your outcome is a sprint time or a peak power value, the marker wants to know how much of any change is measurement noise. The standard reference is Will Hopkins’ 2000 paper in Sports Medicine, which sets out three measures for a test–retest study:
- Typical error of measurement, the within-participant standard deviation between trials, usually expressed as a coefficient of variation in per cent for measures on a ratio scale.
- Intraclass correlation coefficient, the test–retest reliability of rank order between athletes, reported with its confidence interval and with the ICC model stated.
- Limits of agreement from Bland and Altman’s 1986 Lancet method, with the bias and the plot, when you are comparing two instruments or methods rather than two trials.
If your project did not include a test–retest, cite the published reliability for the protocol and equipment you used and state the typical error you are assuming. A change smaller than the typical error is not a change, and saying so in the discussion is worth more marks than any p value. The same logic underlies the internal-consistency reporting explained in our guide to acceptable Cronbach’s alpha values for questionnaire-based projects.
What effect size should a sport science dissertation report?
Cohen’s d (or Hedges’ g for small samples) for differences, partial eta squared for ANOVA effects, and r for associations. Two scales are in circulation and you should name the one you use. Cohen’s conventions of .20, .50 and .80 for small, medium and large are the general-purpose ones. The sport-science-specific thresholds proposed by Hopkins and colleagues in their 2009 Medicine and Science in Sports and Exercise paper on progressive statistics set 0.2, 0.6, 1.2 and 2.0 for small, moderate, large and very large, and add the idea of the smallest worthwhile change, conventionally 0.2 of the between-athlete standard deviation, as the threshold a practical intervention must clear. Report the effect size with a confidence interval, and interpret it against a named scale.
Should you use magnitude-based inference?
Not as your primary analysis. Magnitude-based inference, the approach that reports the probability an effect is beneficial, trivial or harmful, was widely used in sport science journals through the 2010s and still appears on reading lists. Kristin Sainani’s 2018 paper in Medicine and Science in Sports and Exercise, and Welsh and Knight’s statistical review in the same journal in 2015, showed that it produces inflated false-positive rates compared with conventional inference. An undergraduate dissertation that uses MBI as its only inferential method invites a question in the viva or feedback that is hard to answer. If your supervisor works in that tradition, run the conventional test and confidence interval first and present the MBI interpretation as supplementary.
What sample size can you defend with eight athletes?
Within-participant designs are more powerful than between-participant ones for the same number of people, which is why small crossover studies are publishable. But eight participants still detects only large effects, so state the effect size your design could detect at 80% power, using G*Power or the published typical error of your outcome, and treat any non-significant result with a large effect size as underpowered rather than null. The general argument, and the published rules of thumb it replaces, is in our guide to sample size for an undergraduate dissertation.
What do you do when the assumptions fail?
Small sport science samples fail normality and sphericity checks routinely. The fallback is a named non-parametric alternative for the simple designs (Wilcoxon, Friedman) and a correction (Greenhouse–Geisser) for the factorial ones, reported with a sentence explaining why. The full map of what each failed assumption means and what to do about it is in our guide to what to do when your statistical assumptions fail.
The table: design to test
| Design | Example | Parametric test | Non-parametric or correction | Effect size |
|---|---|---|---|---|
| One group, pre and post | Jump height before and after six weeks of plyometrics | Paired-samples t test | Wilcoxon signed-rank | Cohen’s d for paired data |
| Two groups, pre and post | Intervention vs control on 5 km time | Mixed ANOVA or ANCOVA with baseline covariate | Rank-based ANCOVA (rare at UG level) | Partial eta squared or adjusted mean difference with CI |
| Crossover, two conditions | Caffeine vs placebo on time to exhaustion | Paired-samples t test | Wilcoxon signed-rank | Cohen’s d |
| Crossover, three or more conditions | Three warm-up protocols on sprint time | One-way repeated-measures ANOVA | Friedman test | Partial eta squared |
| Condition by time | Lactate at four time points in two conditions | Two-way repeated-measures ANOVA | Greenhouse–Geisser correction | Partial eta squared for the interaction |
| Two independent groups | Forwards vs backs on Yo-Yo test | Independent-samples t test | Mann–Whitney U | Cohen’s d or Hedges’ g |
| Association | Squat 1RM and 20 m sprint time | Pearson’s r | Spearman’s rho | r with CI |
| Test–retest reliability | Two sessions on the same force plate | ICC, typical error, CV% | Bland–Altman limits of agreement | Not applicable |
Which software should you use?
SPSS runs everything in the table and is what most UK sport science departments teach; jamovi runs the same set free of charge and is the sensible choice if your campus licence lapses over the summer; R is the long-term investment. The trade-offs, and what happens to each when your university access ends, are compared in our guide to SPSS versus R versus jamovi. Whichever you use, the reporting sentence is the same: test, degrees of freedom, statistic, exact p, effect size with confidence interval, and, for a performance measure, the change against the typical error.
Where students lose marks on this section
Not on the test. On three sentences that are missing: the one that justifies the design-to-test choice, the one that reports reliability of the measure, and the one that interprets the effect size against a named scale and the smallest worthwhile change. If the analysis is done and the results and methods sections are the blank part, Tesify can draft both from your own design, your own output and your own reliability figures, so the reasoning stays yours and the chapter stops being the bottleneck. Every sentence is 100% written by you; what you get is structure and momentum.
Frequently asked questions
Which statistical test is most common in a sport science dissertation?
The paired-samples t test and the repeated-measures ANOVA, because most designs measure the same athletes more than once. Between-group tests and correlations are the next most common, and reliability statistics appear alongside whichever test you run.
Can I run two paired t tests instead of a mixed ANOVA?
No. Showing that one group changed significantly and the other did not is not a test of whether the groups differed. Use a mixed ANOVA and report the interaction, or an ANCOVA on the post-test with baseline as a covariate.
What is a typical error of measurement?
The within-participant standard deviation between repeated trials on the same test, usually expressed as a coefficient of variation in per cent. Hopkins’ 2000 paper defines it, and a change smaller than the typical error should not be described as a change.
Do I need to report an ICC?
If you ran a test–retest, yes, with its confidence interval and the ICC model used. If you did not, cite the published reliability of the protocol and equipment and state the typical error you are assuming.
Should I use Cohen’s effect-size scale or Hopkins’ scale?
Either, but name it. Cohen’s conventions are general purpose; Hopkins and colleagues’ 2009 thresholds were proposed for sport science and add the smallest worthwhile change. Interpreting an effect against a scale you do not name is the error markers see most.
Is magnitude-based inference acceptable in a dissertation?
Only as a supplement. Published critiques in 2015 and 2018 showed inflated false-positive rates, so run the conventional test and confidence interval first and present any MBI interpretation as additional.
What if my sample is only eight athletes?
Common and defensible for a within-participant design, provided you state the effect size the study could detect at 80% power and interpret non-significant results with large effect sizes as underpowered rather than null.
What should I do if sphericity is violated?
Report Mauchly’s test and apply the Greenhouse–Geisser correction to the degrees of freedom, saying so in the results. It is the expected response, not a problem.
Do I need to report reliability if I used published equipment?
Yes, at least by citation. State the published typical error or ICC for the protocol on the equipment you used, and compare your observed changes to it in the discussion.