Measures of Central Tendency and Dispersion in Psychology

In 1907 Francis Galton lined up 787 guesses of an ox’s weight. The middle guess, the median, was 1,207 lb. The ox weighed 1,198 lb (Galton, 1907). Choosing the right measure of central tendency is not a technicality.

Every psychology study that collects numbers ends up with a list of scores: words recalled, seconds taken, points on a stress scale. A list of 30 numbers tells a reader almost nothing at a glance. Measures of central tendency and dispersion shrink that list down to two facts that matter – what a typical score looks like, and how much people differed from it.

The AQA A-level specification lists these under descriptive statistics: “measures of central tendency – mean, median, mode; calculation of mean, median and mode; measures of dispersion; range and standard deviation; calculation of range” (AQA, 2021). Exam questions then ask you to calculate them, to say which one is most appropriate for a given set of data, and to explain what they suggest about a study’s results. If you are shaky on the difference between nominal, ordinal and interval data, the guide to levels of measurement is worth reading alongside this one, because it decides which average you are allowed to use.

This guide follows the order students usually ask their questions in: what the measures are, how to calculate each one, their strengths and weaknesses, how to choose between them, and how to handle the exam questions. There is a free calculator part-way down that works out the mean, median, mode, range and standard deviation for any set of scores and shows its working. The full step-by-step method for the standard deviation has its own article, so here it gets a shorter treatment.

What Are Measures of Central Tendency in Psychology?

Measures of central tendency in psychology are single values that describe the typical or central score in a set of data. There are three: the mean, the median and the mode. Each one is a different way of answering the question “what is an average score here?”, and each gives a different answer when the data are uneven.

The US National Institute of Standards and Technology describes the task as finding “a typical or central value that best describes the data” and gives the same three definitions (NIST/SEMATECH, n.d.-a):

• The mean is the arithmetic average: add every score together and divide by how many scores there are.
• The median is the middle score once all the scores are put in order from lowest to highest.
• The mode is the score that occurs most often.

In everyday speech “average” nearly always means the mean. In psychology you need to say which average you mean, because the three can tell quite different stories about the same participants.

Where Central Tendency and Dispersion Fit in Research Methods

Measures of central tendency and dispersion are descriptive statistics. They describe and summarise the data a study actually collected, and they only work on quantitative data, meaning data in the form of numbers. They do not tell you whether a difference between two groups is big enough to be trusted. That is the job of inferential statistics, such as the sign test or the Mann-Whitney U test, which come later in the analysis.

In a typical results section, descriptive statistics come first, usually in a table showing the average and spread for each condition. A reader looks at that table, forms a first impression, and then checks the inferential test to see whether the difference is statistically significant.

How to Calculate the Mean, Median and Mode

To calculate the mean, median and mode you only need the raw scores, a pencil and, for the mean, a calculator. The worked examples below use one small set of made-up scores so you can see how the three averages compare. Imagine ten participants were shown a list of 20 words and asked to recall as many as they could. Their scores were:

12, 7, 9, 15, 9, 11, 8, 14, 9, 10

How to Calculate the Mean

The mean is calculated by adding up all the scores and dividing the total by the number of scores. As a formula, mean = total of all scores ÷ number of scores. The mean is often written as x̄ (said “x-bar”).

1. Add the scores: 12 + 7 + 9 + 15 + 9 + 11 + 8 + 14 + 9 + 10 = 104.
2. Count the scores: there are 10.
3. Divide: 104 ÷ 10 = 10.4 words.

The mean does not have to be a score anyone actually got. Nobody recalled 10.4 words, and that is fine. What matters is that the mean uses the exact value of every single score.

How to Calculate the Median

The median is calculated by putting the scores in order from lowest to highest and finding the one in the middle. With an odd number of scores there is one middle score. With an even number there are two, and the median is halfway between them.

1. Put the scores in order: 7, 8, 9, 9, 9, 10, 11, 12, 14, 15.
2. Find the middle position. A quick rule is (n + 1) ÷ 2, where n is the number of scores. Here (10 + 1) ÷ 2 = 5.5, so the median sits halfway between the 5th and 6th scores.
3. The 5th score is 9 and the 6th is 10. Halfway between them is (9 + 10) ÷ 2 = 9.5 words.

If there had been eleven participants, the median would simply be the 6th score in the ordered list, with five scores below it and five above. The most common mistake is forgetting to put the scores in order first, which gives you the middle of the list as written, not the middle score.

How to Find the Mode

The mode is found by counting how often each score appears and picking the one that appears most. In the word recall data, 9 appears three times and every other score appears once, so the mode is 9 words.

Two complications come up in exams. If two scores tie for most frequent, the data are bimodal and you report both. If every score appears exactly once, there is no mode, or at least no useful one. NIST notes that the mode “is not necessarily unique” and is often used loosely, to describe the peaks or “humps” you see in a graph of the data (NIST/SEMATECH, n.d.-a).

Notice that the three averages disagree, even on a tidy set of ten scores. The mean sits above the other two because a couple of participants did unusually well. That pattern – a few extreme scores dragging the mean towards them – is the key to understanding the strengths and weaknesses of each measure.

Strengths and Weaknesses of the Mean, Median and Mode

The strengths and weaknesses of the mean, median and mode come down to a trade-off. The more of the data a measure uses, the more sensitive it is, but the more easily a single odd score can distort it. Exam questions on “advantages and disadvantages” are really asking whether you understand that trade-off.

Strengths and Weaknesses of the Mean

The main strength of the mean is that it uses the exact value of every score, which makes it the most sensitive of the three averages. A change to any one score changes the mean. It is also the average that most other statistics are built on, including the standard deviation and parametric tests such as t-tests.

The main weakness of the mean is that the same sensitivity makes it easy to distort. Leys et al. (2013) used a set of eight scores to show how badly: 1, 3, 3, 6, 8, 10, 10 and 1000. The mean is 130.13, a value that describes none of the participants. Seven of the eight scores sit between 1 and 10. Take the 1000 away and the mean drops to about 5.9. A score that far from the rest is called an outlier, and it might be a real but unusual person, a participant who misunderstood the task, or a typing error.

The mean also cannot be used sensibly with categories, and is questionable with ordinal data such as rating scales, as the section on choosing a measure explains below.

Strengths and Weaknesses of the Median

The main strength of the median is that it is not affected by extreme scores. In the Leys et al. (2013) data the median is 7 whether the last score is 10, 1000 or a million, because the median only cares about which score is in the middle position, not how far away the end scores are. Leys and colleagues express this with the idea of a “breakdown point”: a single infinite score makes the mean infinite, but the median only becomes absurd “when more than 50% of the observations are infinite”.

This is exactly why Galton chose the median for the ox-weighing competition. He called it the “middlemost” estimate and argued that, on a one-vote-one-value principle, every other estimate would be outvoted by a majority as too high or too low. The median of the 787 guesses was 0.8% above the true weight (Galton, 1907).

The same logic is why the UK’s Office for National Statistics reports median pay. Median weekly earnings for full-time employees were £766.60 in April 2025, and the ONS describes the median as “our preferred measure of average earnings, as it is less affected by a relatively small number of very high earners than the mean” (Office for National Statistics, 2025).

The weakness of the median is the flip side of that strength. It ignores the exact values of all the scores except the middle one or two, so it is less sensitive than the mean. Two groups could have very different sets of scores and still share a median. It is also slightly more awkward to calculate by hand with large data sets, since everything has to be put in order first.

Strengths and Weaknesses of the Mode

The main strength of the mode is that it is the only average that works with categories. If you observe which toy each child picks first, you cannot add up “car” and “doll” and divide by two, but you can say the car was the most popular choice. The mode is also unaffected by extreme scores and is very quick to find.

The weaknesses of the mode are that it is crude and can be unstable. It ignores everything except the most frequent value, there may be two modes or none at all, and in a small sample one extra participant can change it completely. In the Leys et al. (2013) data, 3 and 10 each appear twice, so the data are bimodal and the mode tells you very little.

Which Measure of Central Tendency Should You Use?

The measure of central tendency you should use depends on two things: the level of measurement of the data, and whether the data contain extreme scores. Ask the questions in that order. The first rules some averages out altogether; the second decides between those that are left.

Step 1: Check the Level of Measurement

• Nominal data (categories, such as yes/no or which toy was chosen): use the mode. The categories have no order, so there is no middle and nothing to add up.
• Ordinal data (ranks or ratings, such as a 1-5 agreement scale): use the median. The scores have an order, so there is a middle, but the gaps between points on the scale are not known to be equal.
• Interval or ratio data (measurements on a scale with equal units, such as time in seconds or words recalled): the mean is usually best, unless step 2 says otherwise.

Rating scales are where students most often slip, because the answers look like numbers. Jamieson (2004) points out that Likert scales, the strongly-disagree-to-strongly-agree scales used in many questionnaires, are ordinal, and that “for ordinal data one should employ the median or mode as the ‘measure of central tendency'”. Her memorable summary, borrowed from other statisticians, is that the average of “fair” and “good” is not “fair-and-a-half”.

Not every statistician agrees. Norman (2010) argued that decades of studies show parametric statistics are robust enough to use with rating scale data, and in practice many published papers report means for Likert items. For A-level, though, the safe and expected answer is the median for ordinal data – and it is well worth mentioning that this is a debated point if a longer evaluation question gives you room.

Step 2: Look for Outliers and Skew

If the data are interval or ratio, the next question is whether any scores sit far away from the rest. If they do, use the median rather than the mean, because the mean will be pulled towards the extreme scores and will no longer describe a typical participant.

A quick check is to calculate both the mean and the median. If they are close, the data are roughly balanced and the mean is fine. If the mean is noticeably higher or lower than the median, something is pulling it. When the whole distribution leans to one side rather than just having one odd score, it is described as skewed. Where the three averages fall on a lopsided distribution is covered in detail in the guide to normal and skewed distributions.

A Warning About the Median in Small Samples

The median is not a perfect fix, and one well-known study shows why. Miller (1988) looked at reaction times, which are nearly always bunched up at the fast end with a long tail of slow responses. He found that with such skewed data, sample medians “tend to over-estimate population medians”, and that the bias grows as the sample shrinks – by up to about 50 milliseconds in his simulations. Comparing medians from conditions with different numbers of trials could therefore create a difference that is not really there.

You will not be asked about Miller’s study at A-level, but it makes a strong evaluation point: no single average is right for every data set, and researchers need to justify the one they pick.

What Are Measures of Dispersion in Psychology?

Measures of dispersion in psychology are statistics that describe how spread out a set of scores is – whether participants scored much the same as each other, or varied a lot. For A-level Psychology the two measures of dispersion on the specification are the range and the standard deviation (AQA, 2021). You may also meet the interquartile range and the variance, which are covered further down.

An average on its own hides a great deal. Picture two classes that both have a mean test score of 60%. In one, every student scored between 55% and 65%. In the other, half the class scored around 30% and half around 90%. Same average, completely different classes – and a teacher would teach them very differently. A measure of dispersion is what tells them apart.

Researchers at Autodesk made the same point in a memorable way. Matejka and Fitzmaurice (2017) built a collection of data sets, the “Datasaurus Dozen”, that look nothing like each other when plotted – one is a drawing of a dinosaur, others form stars, circles and parallel lines – yet every one has “the same summary statistics (mean, standard deviation, and Pearson’s correlation) to two decimal places”. Summary numbers are a starting point, not a substitute for looking at the data.

How to Calculate the Range

The range is calculated by subtracting the lowest score from the highest score. It is the simplest measure of dispersion and the only one you are specifically required to calculate for AQA, alongside understanding the standard deviation (AQA, 2021).

For the word recall data, the highest score is 15 and the lowest is 7, so the range is 15 − 7 = 8 words. The range is always a single number, not the two end points. Writing “the range is 7 to 15” is a common way to lose a mark.

AQA’s own mark schemes use the same highest-minus-lowest method. In the June 2023 A-level paper, a mark scheme example describes most students giving a stress rating of 4 and one giving a rating of 10, which “could result in the range of 6” (AQA, 2023).

Strengths and Weaknesses of the Range

The strength of the range is that it is quick and easy to calculate, and easy for anyone to understand. It gives a fast first impression of how widely scores varied.

The weakness of the range is that it is based on just two scores, the most extreme ones. NIST puts it bluntly: “The spread near the center of the data is not captured at all” (NIST/SEMATECH, n.d.-b). The June 2023 mark scheme gives the same limitations as creditworthy answers: the range values “only take the two extreme stress ratings into account”, are “not calculated using each individual stress rating” and “can be distorted by outliers” (AQA, 2023). In the Leys et al. (2013) data, one score of 1000 stretches the range to 999, even though seven of the eight scores lie within 9 points of each other.

Standard Deviation: The Measure of Dispersion That Uses Every Score

The standard deviation is a measure of dispersion that tells you, roughly, how far a typical score lies from the mean. A small standard deviation means the scores are bunched closely around the mean; a large one means they are widely spread. Unlike the range, it is calculated from every score in the data set.

Here is a short worked example using five scores: 3, 5, 6, 7 and 9.

1. Find the mean: 30 ÷ 5 = 6.
2. Subtract the mean from each score: −3, −1, 0, 1, 3.
3. Square each of those differences: 9, 1, 0, 1, 9.
4. Add the squares: 9 + 1 + 0 + 1 + 9 = 20.
5. Divide by the number of scores minus one: 20 ÷ 4 = 5. This figure is the variance.
6. Take the square root: √5 = 2.24.

Dividing by n − 1 rather than n gives the sample standard deviation, which is the version used when your participants are a sample from a larger population. NIST’s formula uses N − 1 for this reason (NIST/SEMATECH, n.d.-b). Dividing by n instead gives 2.00 here. The standard deviation guide explains the difference in full and walks through a larger example.

Strengths and Weaknesses of the Standard Deviation

The main strength of the standard deviation is that it uses every score, so it gives a much fuller picture of spread than the range. It is also expressed in the same units as the original data, and it links directly to the normal distribution and to z-scores. The AQA specification gives an example of an exam task: “explaining why the standard deviation might be a more useful measure of dispersion for a given set of scores, eg where there is an outlying score” (AQA, 2021). The reasoning is that the range is built entirely from the extremes, so one outlier changes it completely, while the standard deviation spreads that outlier’s influence across all the scores.

The standard deviation is not immune to outliers, though, and this is its main weakness. Because every difference is squared, one far-out score has a very large effect. In the Leys et al. (2013) example the standard deviation is 328.80 with the 1000 included (using n), against about 3.4 without it. It is also harder to calculate by hand than the range, and it is only meaningful for interval or ratio data where the mean is appropriate in the first place.

Other Measures of Dispersion: Interquartile Range and Variance

The four measures of dispersion most students meet are the range, the interquartile range, the variance and the standard deviation. Only the range and standard deviation are named in the AQA specification, but the other two appear in textbooks, university courses and published papers.

• Interquartile range (IQR). The spread of the middle half of the scores: the value three-quarters of the way up the ordered list minus the value one-quarter of the way up. Because it ignores the top and bottom quarters, it is far less affected by outliers than the range. In the Leys et al. (2013) data the IQR is 10 − 3 = 7, which describes the bulk of the scores well. Different textbooks and software use slightly different rules for finding the quartiles, so small data sets can give slightly different answers.
• Variance. The standard deviation before the final square root – 5 in the worked example above. It is used heavily in more advanced statistics, but because it is in squared units it is harder to interpret, which is why results sections usually report the standard deviation instead.
• Median absolute deviation (MAD). A measure of spread built around the median rather than the mean. Leys et al. (2013) found that none of the 127 relevant articles they surveyed in two leading psychology journals used it, and recommended it for detecting outliers because, like the median, it is barely affected by them. They note the IQR can only cope with up to 25% of the data being contaminated.

A useful rule of thumb is to pair each average with the spread measure that shares its strengths. The mean goes with the standard deviation, both built from every score. The median goes with the interquartile range, both built from the position of scores in an ordered list.

Mean, Median, Mode, Range and Standard Deviation Calculator

This calculator works out the mean, median, mode, range and standard deviation for any set of scores, and shows the working for each, so you can use it to check your own answers. Tell it the level of measurement and it will also suggest which average and which measure of dispersion to report.

How to Answer Central Tendency and Dispersion Exam Questions

Central tendency and dispersion exam questions fall into four types: calculate a value, say which measure is most appropriate, interpret what the figures suggest, and evaluate a measure. Each type has a pattern that picks up the marks.

1. Calculation Questions

Calculation questions usually carry one or two marks. Show your working, keep the units (words, seconds, points), and round only at the end. The AQA specification includes using “an appropriate number of significant figures” among its maths skills (AQA, 2021), so if a question asks for a set number of decimal places or significant figures, give exactly that.

2. “Which Measure Is Most Appropriate?” Questions

For “which measure is most appropriate” questions, name the measure and give a reason tied to the data in front of you. “The median, because it is not affected by extreme values” is generic. “The median, because Participant 7’s score of 58 is far higher than everyone else’s and would inflate the mean” uses the stem, and that is what the application marks reward.

3. “What Do These Results Suggest?” Questions

“What do the results suggest” questions ask you to interpret an average and a measure of spread, with a justification for each. The June 2023 A-level mark scheme shows the pattern: one mark for saying what the medians suggest about the effect of the independent variable, one for justifying it with the figures, then the same again for the range (AQA, 2023). The AS mark scheme from June 2022 awarded marks for saying there was “less dispersion/variation in/spread (around the mean)” of heart rate after meditation, justified by the smaller standard deviation (AQA, 2022).

Here is a made-up example to practise on. A student tests whether revising with music affects recall of a 20-word list, using ten participants in each condition.

A full-mark answer might say: the means suggest music made little difference to recall, because the averages of 10.4 and 10.6 words are almost identical. However, the standard deviations suggest music affected people differently, because scores in the music condition were far more spread out (4.33 compared with 2.32). Some participants may have been helped by the music and others distracted by it. The larger range in condition B (14 against 8) supports this.

4. Evaluation Questions

Evaluation questions such as “explain one limitation of using the range” want a limitation applied to the study in the question, not a textbook sentence. State the limitation, apply it to the actual scores, and explain the consequence. For example: the range only uses the highest and lowest stress ratings, so if one participant who hated running gave a rating of 10, the range would suggest far more variation than most of the class actually showed.

Common Mistakes With Measures of Central Tendency and Dispersion

The most common mistakes with measures of central tendency and dispersion are small slips that cost marks every year. Check your answers against this list:

• Not ordering the scores before finding the median. The median is the middle of the ordered list.
• Giving the range as two numbers. The range is one number: highest minus lowest.
• Using the mean with ratings or categories. Use the median for ordinal data and the mode for nominal data.
• Calling the standard deviation an average. It is a measure of spread, not of central tendency.
• Reporting an average with no measure of spread. Two groups with the same mean can be completely different.
• Treating descriptive statistics as proof. A difference between two means tells you nothing about statistical significance until an inferential test has been run.
• Rounding too early. Keep full figures through the calculation and round the final answer only.

Once you have described your data, the next decision is which inferential test to run. The level of measurement you used to choose your average also drives that choice, and the guide to choosing a statistical test sets out the whole decision in one place. Ordinal data, for example, lead towards tests such as the Mann-Whitney U test or Wilcoxon signed-rank test, which work on ranks – in effect, on the same ordering that gives you the median.

Conclusion

Measures of central tendency tell you what a typical score looks like, and measures of dispersion tell you how much scores varied around it. You need both. The mean is the most sensitive average but is easily dragged by extreme scores; the median resists them; the mode is the only option for categories. The range is quick but relies on just two scores, while the standard deviation uses every score and is the better measure of spread for most interval data.

When you choose, start with the level of measurement, then check for outliers, and always justify your choice using the data in front of you. Galton’s median came within 9 lb of an ox’s true weight because he picked the average that suited his data. That is the skill the exam is testing.

Frequently Asked Questions

What are the 3 measures of central tendency in psychology?

The three measures of central tendency in psychology are the mean, the median and the mode. The mean adds all scores and divides by how many there are, the median is the middle score once they are in order, and the mode is the most frequent score. All three are ways of describing a typical score.

What are the 2 measures of dispersion in psychology?

The two measures of dispersion on the AQA A-level Psychology specification are the range and the standard deviation. The range is the highest score minus the lowest. The standard deviation shows how far scores typically lie from the mean, and is calculated from every score, which makes it more informative.

Which measure of central tendency is most affected by outliers?

The mean is the measure of central tendency most affected by outliers, because every score’s exact value goes into the total. A single extreme score can pull the mean well away from where most participants scored. The median and mode are not affected by how far away an extreme score is.

Which measure of central tendency is best?

No single measure of central tendency is always best. The mean suits interval or ratio data with no extreme scores, the median suits ordinal data or data with outliers, and the mode is the only choice for categories. The best measure is the one that fits the data and gives a fair picture of a typical participant.

Is standard deviation a measure of central tendency?

No. Standard deviation is a measure of dispersion, also called spread or variability. It describes how far scores are spread around the mean, whereas measures of central tendency describe where the middle of the scores lies. The two are usually reported together, typically the mean with its standard deviation.

What are the 4 measures of dispersion?

The four commonly taught measures of dispersion are the range, the interquartile range, the variance and the standard deviation. The range and interquartile range are based on positions in the ordered scores; the variance and standard deviation are based on distances from the mean. A-level Psychology requires only the range and standard deviation.

What is the range in psychology?

In psychology, the range is the simplest measure of dispersion: the difference between the highest and lowest scores in a data set. If scores run from 4 to 19, the range is 15. It is quick to calculate but uses only two scores, so a single outlier can make it misleading.

What is not a measure of dispersion?

The mean, median and mode are not measures of dispersion. They are measures of central tendency, which describe a typical score rather than how varied the scores are. Measures of dispersion include the range, interquartile range, variance and standard deviation.

References

• AQA. (2021). AS and A-level Psychology specification (7181, 7182) (Version 1.2). Assessment and Qualifications Alliance.
• AQA. (2022). AS Psychology 7181/2 Paper 2 Psychology in context: Mark scheme, June 2022. Assessment and Qualifications Alliance.
• AQA. (2023). A-level Psychology 7182/2 Paper 2 Psychology in context: Mark scheme, June 2023. Assessment and Qualifications Alliance.
• Galton, F. (1907). Vox populi. Nature, 75(1949), 450–451.
• Jamieson, S. (2004). Likert scales: How to (ab)use them. Medical Education, 38(12), 1217–1218.
• Leys, C., Ley, C., Klein, O., Bernard, P., & Licata, L. (2013). Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median. Journal of Experimental Social Psychology, 49(4), 764–766.
• Matejka, J., & Fitzmaurice, G. (2017). Same stats, different graphs: Generating datasets with varied appearance and identical statistics through simulated annealing. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (pp. 1290–1294). ACM.
• Miller, J. (1988). A warning about median reaction time. Journal of Experimental Psychology: Human Perception and Performance, 14(3), 539–543.
• NIST/SEMATECH. (n.d.-a). Measures of location. In e-Handbook of statistical methods. National Institute of Standards and Technology.
• NIST/SEMATECH. (n.d.-b). Measures of scale. In e-Handbook of statistical methods. National Institute of Standards and Technology.
• Norman, G. (2010). Likert scales, levels of measurement and the “laws” of statistics. Advances in Health Sciences Education, 15(5), 625–632.
• Office for National Statistics. (2025). Employee earnings in the UK: 2025. ONS.

Further Reading and Research

Recommended Articles

• Standard Error Explained: Formula, Meaning and SD vs SE
• What Is a Correlation Coefficient? Pearson’s r and Psychology Statistics
• IQ Scale Explained: Understanding Intelligence Test Scores

Suggested Books

• Coolican, H. (2019). Research methods and statistics in psychology (7th ed.). Routledge.
  • A long-standing favourite for A-level and undergraduate students, covering descriptive statistics, levels of measurement and choosing tests in plain language.
• Field, A. (2024). Discovering statistics using IBM SPSS Statistics (6th ed.). SAGE.
  • The standard university text for psychology statistics, with clear early chapters on the mean, spread and the effect of outliers, written in an informal style.
• Spiegelhalter, D. (2019). The art of statistics: Learning from data. Pelican.
  • A readable, non-technical tour of how statistics works in the real world, including why the choice of average can change the story a number tells.

Recommended Websites

• NIST/SEMATECH e-Handbook of Statistical Methods
  • A free, authoritative US government reference with short, clear pages on measures of location and measures of scale.
• AQA A-level Psychology past papers and mark schemes
  • Official papers and mark schemes, which show exactly how questions on averages and spread are worded and credited.
• Autodesk Research: Same Stats, Different Graphs
  • The Datasaurus Dozen, with animations showing how very different data sets can share the same mean and standard deviation.

Kathy Brodie

Kathy Brodie is an Early Years Professional, Trainer and Author of multiple books on Early Years Education and Child Development. She is the founder of Early Years TV and the Early Years Summit.

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To cite this article please use:

Early Years TV Measures of Central Tendency and Dispersion in Psychology. Available at: https://www.earlyyears.tv/measures-of-central-tendency-dispersion-psychology/ (Accessed: 11 October 2026).