In simple terms
A friendly intro before the formal notes — no formulas yet.
From Stopwatch to Strategy
First, we collect raw data about an athlete's performance, which is the 'measurement'. Then, we use that data to make an informed judgement about their ability or progress, which is the 'evaluation'.
Imagine you're a chef baking a cake. Following the recipe and weighing the flour and sugar is 'measurement' – you're just collecting quantitative data. Tasting the finished cake and deciding if it's delicious, needs more sugar, or is good enough to sell is the 'evaluation' – you're making a qualitative judgement based on the data.
- 1
Select the right test: Choose a valid and reliable test to measure a specific fitness component, like the multistage fitness test for aerobic capacity.
- 2
Collect the data: Perform the test under controlled conditions to ensure objectivity and gather the raw scores, which are the measurements.
- 3
Analyse the numbers: Calculate statistics like the mean and standard deviation to summarise the performance of an individual or a group.
- 4
Evaluate and decide: Compare the results to norms or previous scores to make a judgement (evaluation) about performance and inform future training decisions.
Explore the concept
Use the live diagram, PhET or GeoGebra sim, and synced steps — play it, drag controls, or tap a step.
Step 1
Select the right test: Choose a valid and reliable test to measure a specific fitness component, like the multistage fitness test for aerobic capacity.
1 more simulation for this topic — run them in the Simulations section below
Simulations
Every simulation here runs the real model — try the steps on a card, then check what you see against the notes.
1 simulation
- GeoGebraCoreIB 2.1
Visual Demo of Standard Deviation
Twenty lettered points are test scores on a number line; drag any of them left or right and the average and standard deviation update.
Try this
- Read the starting average and standard deviation for the squad's scores.
- Drag one athlete's score far to the right and compare how much the standard deviation jumps with how little the average moves.
- Bunch every score close together, then spread them out, and compare the two standard deviations.
- Each time, divide the standard deviation by the average to get the coefficient of variation.
Look for Standard deviation measures how far scores sit from the mean: a consistent squad has a small value, and a single outlier inflates it.
Terry Lee Lindenmuth · GeoGebra · GeoGebra Terms of Service
Key formulas
Tap any symbol to reveal exactly what it means and its units.
Full topic notes
Formal explanation with the rigour you need for the exam.
Core Principles of Performance Testing
To ensure that the data we collect is meaningful, any test we use must adhere to three critical principles: validity, reliability, and objectivity. Validity ensures the test is appropriate for its purpose. Reliability ensures the results are consistent and repeatable. Objectivity ensures the results are not influenced by the person conducting the test. Often, there is a trade-off; for example, a highly controlled laboratory test might have excellent validity and reliability but be impractical for a large team, whereas a simpler field test is more practical but may be less precise.
Validity: Does the test measure what it's supposed to measure? (e.g., using a vertical jump to test anaerobic power).
Reliability: Can the test produce consistent results on re-testing? (e.g., an athlete scoring similarly on the Illinois Agility Test on two consecutive days).
Objectivity: Is the measurement free from the bias of the tester? (e.g., using timing gates instead of a handheld stopwatch).
Descriptive Statistics: Summarising Performance Data
Once we have our measurements, we need to make sense of them. Descriptive statistics allow us to summarise large amounts of data into a few key numbers. Measures of central tendency (mean, median, mode) tell us about the 'typical' score, while measures of dispersion (range, standard deviation) tell us how spread out the scores are. Standard deviation is particularly powerful as it quantifies the consistency of the performance.
Standard Deviation (s) =
Inferential Statistics: Comparing Performance Data
Descriptive statistics are great for summarising one group, but often we want to compare two different groups (e.g., sprinters vs. endurance runners) or the same group at two different times (e.g., before and after a training programme). This is where inferential statistics, such as the t-test, become essential. A t-test determines whether the difference between two mean scores is 'statistically significant' or if it could have just happened by random chance.
Unpaired t-test: Compares the means of two independent groups (e.g., comparing the grip strength of climbers and swimmers).
Paired t-test: Compares the means of the same group at two different times (e.g., comparing an athlete's body fat percentage before and after a diet).
Significance (p-value): A result is typically considered statistically significant if the p-value is less than 0.05 (). This means there is less than a 5% probability the difference is due to chance.
For t-test questions, don't just state the result. You must interpret it in the context of the question. For example, 'The paired t-test showed a significant difference in 1RM squat strength pre- and post-training (p < 0.05), indicating that the 8-week resistance training programme was effective in increasing lower body strength.' This shows you understand the application.
Worked examples
See the formulas applied — reveal one step at a time, like the exam.
A group of 6 rugby players performed a 20m sprint test. Their times (in seconds) were: 2.8, 2.9, 3.0, 3.1, 3.2, 3.4. Calculate the mean and standard deviation for this data set.
- 1
Calculate the Mean ():
A coach wants to compare the consistency of two netball shooters. Shooter A has a mean score of 15 goals per game with a standard deviation of 3. Shooter B has a mean score of 25 goals per game with a standard deviation of 4. Which shooter is more consistent? Use the Coefficient of Variation (CV) to justify your answer.
- 1
Recall the formula for Coefficient of Variation (CV):
How it all connects
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Tap a linked idea to see how it connects back to the main topic — that connection is what examiners reward.
Glossary
Key terms for this topic — skim now; the Check step will test them.
- Measurement
The process of collecting quantitative data. It involves recording a score or value, such as time in seconds, distance in metres, or number of repetitions. For example, recording a 100m sprint time of 11.2 seconds.
- Evaluation
The process of making a judgement about the quality of a measurement. It involves interpreting the data by comparing it to norms, previous results, or goals. For example, deciding that a sprint time of 11.2 seconds is 'excellent' for a school athlete.
- Validity
The degree to which a test measures what it claims to measure. A valid test for aerobic capacity, like the VO₂ max test, actually measures aerobic capacity, not something else like leg power.
- Reliability
The degree of consistency of a measure. A reliable test will produce similar results when repeated under the same conditions with the same subjects. A common trap is confusing it with validity; a test can be reliable but not valid (e.g., consistently wrong).
- Objectivity
The degree to which a test is free from bias. An objective test will produce the same result regardless of who is administering it. Electronic timing is more objective than manual stopwatch timing.
- Standard Deviation (SD)
A measure of the spread or dispersion of a set of data from its mean. A low SD indicates that the data points tend to be close to the mean, suggesting high consistency. A high SD indicates the data points are spread out over a wider range.
- Coefficient of Variation
A relative measure of dispersion, calculated as (SD / Mean) x 100%. It is useful for comparing the variability of two or more data sets with different units or different means.
Name it
Read the meaning, then pick which of this lesson’s terms it describes. Miss one and you see what your choice really means.
A measure of the spread or dispersion of a set of data from its mean. A lowindicates that the data points tend to be close to the mean, suggesting high consistency. A highindicates the data points are spread out over a wider range.
Quick check
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Teach it back
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Teach it back
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Revision flashcards
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Key takeaways
Review these before you close the topic — retrieval beats re-reading.
Validity: Does the test measure what it's supposed to measure? (e.g., using a vertical jump to test anaerobic power).
Reliability: Can the test produce consistent results on re-testing? (e.g., an athlete scoring similarly on the Illinois Agility Test on two consecutive days).
Objectivity: Is the measurement free from the bias of the tester? (e.g., using timing gates instead of a handheld stopwatch).
Practice — then mark it
The whole point: a real Cambridge question, marked mark-by-mark.
Test Your Knowledge on Measurement and Evaluation
Test Your Knowledge on Measurement and Evaluation
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Checkpoint
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