Distributions (AQA A Level Psychology)

Normal distribution

• Distribution in psychology refers to the spread of data around the mean for a specific sample or population

• Researchers in psychology are interested in the extent to which one data set varies from the mean

  • Do most scores cluster around the mean?

  • Are the scores spread symmetrically?

  • Are they skewed?

• A normal distribution is symmetrical around the mean, with most scores being close to it, showing a peak in the middle where the mean value is located

• The shape of a normal distribution is known as the ‘bell curve’ as the measurement outline looks like a bell

• Most scores (when the data is normally distributed) will fall within the central part of the bell curve

• Extreme outliers will fall within the 'tail ends' of the curve (small scores on the extreme left, high scores on the extreme right of the curve)

• The tail ends never actually touch the x-axis as there is no assumption as to there being one, final extreme high or low score

• Examples of data that is normally distributed are height, weight, shoe size

• The normal distribution can be used to test for signs of deviance from the norm e.g.

  • people who score beyond two standard deviations of the mean may rank as having an extremely high or low scoring, such as

    • IQ

    • scoring high on a scale to indicate postpartum depression

    • scoring low on an empathy scale which tests for psychopathy

Skewed distributions

• In a perfect normal distribution the mean, mode and median all appear at the peak of the curve i.e. they have similar values

  • Scores to the left of the peak represent people who have scored less than the mean

  • Scores to the right of the peak represent people who have scored more than the mean

• There are some behaviours/conditions/test scores which do not fit neatly into a normal distribution, this represents skewed distributions

• A skewed distribution describes a graph curve where one tail is longer than the other

  • There is asymmetry in the graph curve, it is not bell-shaped

  • The two halves of the distribution do not mirror each other because the data is not distributed equally on both sides of the distribution’s peak

  • The mean is the measure of central tendency which is most affected by skewed distributions as it takes all scores in the data set into account

Positive skew

• A positive skew is one in which most of the values are found towards the left side of the graph, giving a long tail on the right

Examples of positively skewed data

• The age at which people take on their first job

  • In a population aged 16-80, most scores will likely be at the lower end of the measure

• A very difficult maths test might see most students score at the lower end of the mark scale

  • As the test is so difficult there are very few in the class who scored towards the right end of the tail (where the high scores reside)

Negative skew

• A negative skew is one in which most of the values are found towards the right side of the graph, giving a long tail on the left

Examples of negatively skewed data

• The age at which people retire

  • In a population aged 16-80 most scores will likely be at the higher end of the scale

• A very easy maths test might see most students score at the higher end of the mark scale

  • As the test is so easy there are very few in the class who scored towards the left end of the tail (where the low scores reside)