1. Z-Scores

2. Histogram A bar chart of a frequency distribution. 0 — 2 1 — 3 2 — 0 3 — 2 4 — 4 5 — 3 6 — 3 7 — 5 8 — 3 9 — 2 10—2

3. Normal Distribution With some frequency distributions the histograms have lots of examples in the middle and fewer that are higher and lower.

4. Normal Distribution Measurements based on an underlying random phenomena distribute normally if you have enough data points.

5. 0 2 4 6 8 10 12 12345678910111213141516171819202122232425262728 Normal Distribution

6. 6 8 10 12 Normal Distribution A Normal Curve is the theoretical line that represents all of the responses in a normal distribution. The area under the curve encloses the frequency distribution of the normally distributed phenomena. 4 0 2 12345678910111213141516171819202122232425262728

7. Characteristics of Normal Curves A line connecting the tops of the bars of a histogram. Mean, median and mode appear at the same point. Lots of responses in the middle fewer in the extremes.

8. One More Interesting Characteristic of Normal Curves 1 Standard Deviation Mean

9. 1 Standard Deviation Mean 50.0 One More Interesting Characteristic of Normal Curves

10. 1 Standard Deviation Mean 50.034.13 One More Interesting Characteristic of Normal Curves

11. 2 Standard Deviations Mean 34.1350.0 13.59 One More Interesting Characteristic of Normal Curves

12. A test score Cognitive abilities are normally distributed.

13. A test score Then, if the tests are designed carefully the assessment of cognitive ability should be normally distributed as well.

14. A test score Results from a standardized test are, by definition, normal distributed.

15. Mean -2-3123 z Scores SD If you know the percentage of scores that are lower than the target score you will know the Percentile Rank of the target score. A test score

16. Normal Curves 2 Standard Deviations Mean 34.1350.0 13.59 1. The relationship between SD and percentage of the area under the curve is constant regardless of the distribution. 2. If the mean and SD of the distribution are known then the percentage of scores lower than every possible score can be computed. 3. In other words, percentile rank of every score can be computed.

17. z Score Computation Subtract the mean of the distribution from the target score. Divide by the standard deviation. Look up the z score on the z table.

18. -2-3123 Target Score (28) Mean (25.69) Number of SD away from the mean Percentage of scores lower than the target score SD Subtract the mean of the distribution from the target score. Divide by the standard deviation. Look up the z score on the z table. 1 SD (2.72)

19. z Score Target score (28) Score - Mean (28 - 25.69 = 2.31) Result / SD (2.31 / 2.72 =.85) Look up on z table (0.85) z = 0.85; area =.80 Score is in the 80th percentile

20. -2-3123 28 z Scores 25.69.85 SD 80 % of scores SD

21. Mean -2-3123 Target Score z Scores SD

22. -2-3123 z Scores SD 25.69 23 -.99 SD 16 % of scores

23. z Score Target score (23) Score - Mean (23 - 25.69 = -2.69) Result / SD (-2.69 / 2.72 = -.99) Look up on z table (-.99) Z = -0.99; area =.16 Score is in the 16th percentile

24. -2-3123 z Scores SD 25.69 23 -.99 SD 16 % of scores

25. Percentile Rankings z Scores Assume a normal distribution Based on knowing everyone in the population Allows comparison of individual to the whole

26. What is Molly’s percentile ranking on the test? What is Karl’s 34 56 22 31 44 47 37 42 51 37 40 29 36 39 52 39 29 51 45 47 42 Molly Karl

27. Bobby and Sally take a standardized test that has 160 questions Bobby gets a raw score of 140 and has a percentile rank of 52. Sally gets a raw score of 142 and has a percentile rank of 67. This doesn’t make sense to Bobby’s mother. First, how can 140 out of 160 be 52nd percentile. And second, why should just a couple of points on the test make such a huge percentile rank difference? What are you going to tell her?