1. The Normal Distribution: The Normal curve is a mathematical abstraction which conveniently describes ("models") many frequency distributions of scores in real-life.

2. length of pickled gherkins: length of time before someone looks away in a staring contest:

3. Francis Galton (1876) 'On the height and weight of boys aged 14, in town and country public schools.' Journal of the Anthropological Institute, 5, 174-180:

5. An example of a normal distribution - the length of Sooty's magic wand... Length of wand Frequency of different wand lengths

6. Properties of the Normal Distribution: 1. It is bell-shaped and asymptotic at the extremes.

7. 2. It's symmetrical around the mean.

8. 3. The mean, median and mode all have same value.

9. 4. It can be specified completely, once mean and s.d. are known.

10. 5. The area under the curve is directly proportional to the relative frequency of observations.

11. e.g. here, 50% of scores fall below the mean, as does 50% of the area under the curve.

12. e.g. here, 85% of scores fall below score X, corresponding to 85% of the area under the curve.

13. Relationship between the normal curve and the standard deviation: All normal curves share this property: the s.d. cuts off a constant proportion of the distribution of scores:- -3 -2 -1 mean +1 +2 +3 Number of standard deviations either side of mean frequency 99.7%68%95%

14. About 68% of scores will fall in the range of the mean plus and minus 1 s.d.; 95% in the range of the mean +/- 2 s.d.'s; 99.7% in the range of the mean +/- 3 s.d.'s. e.g.: I.Q. is normally distributed, with a mean of 100 and s.d. of 15. Therefore, 68% of people have I.Q's between 85 and 115 (100 +/- 15). 95% have I.Q.'s between 70 and 130 (100 +/- (2*15). 99.7% have I.Q's between 55 and 145 (100 +/- (3*15).

15. 85 (mean - 1 s.d.) 115 (mean + 1 s.d.) 68%

16. Just by knowing the mean, s.d., and that scores are normally distributed, we can tell a lot about a population. If we encounter someone with a particular score, we can assess how they stand in relation to the rest of their group. e.g.: someone with an I.Q. of 145 is quite unusual: this is 3 s.d.'s above the mean. I.Q.'s of 3 s.d.'s or above occur in only 0.15% of the population [ (100-99.7) / 2 ].

17. z-scores: z-scores are "standard scores". A z-score states the position of a raw score in relation to the mean of the distribution, using the standard deviation as the unit of measurement.

18. 1. Find the difference between a score and the mean of the set of scores. 2. Divide this difference by the s.d. (in order to assess how big it really is).

19. Raw score distributions: A score, X, is expressed in the original units of measurement: z-score distribution: X is expressed in terms of its deviation from the mean (in s.d's) X = 65 X = 236 z = 1.5

20. 55 70 85 100 115 130 145 z-scores transform our original scores into scores with a mean of 0 and an s.d. of 1. Raw I.Q. scores (mean = 100, s.d. = 15):

21. -3 -2 -1 0 +1 +2 +3 I.Q. as z-scores (mean = 0, s.d. = 1). z for 100 = (100-100) / 15 = 0, z for 115 = (115-100) / 15 = 1, z for 70 = (70-100) / -2, etc.

22. Conclusions: Many psychological/biological properties are normally distributed. This is very important for statistical inference (extrapolating from samples to populations - more on this in later lectures...) z-scores provide a way of (a) comparing scores on different raw-score scales; (b) showing how a given score stands in relation to the overall set of scores.