1. The Normal Curve, Standardization and z Scores Chapter 6

2. The Bell Curve is Born (1769)

3. A Modern Normal Curve

4. Development of a Normal Curve: Sample of 5

5. Development of a Normal Curve: Sample of 30

7. Development of a Normal Curve: Sample of 140

8. >As the sample size increases, the shape of the distribution becomes more like the normal curve. >Can you think of variables that might be normally distributed? Think about it: Can nominal (categorical) variables be normally distributed?

9. Standardization, z Scores, and the Normal Curve >Standardization: allows comparisons z distribution Comparing z scores

10. The z Distribution

11. Transforming Raw Scores to z Scores >Step 1: Subtract the mean of the population from the raw score >Step 2: Divide by the standard deviation of the population

12. Transforming z Scores into Raw Scores >Step 1: Multiply the z score by the standard deviation of the population >Step 2: Add the mean of the population to this product

13. Using z Scores to Make Comparisons >If you know your score on an exam, and a friend’s score on an exam, you can convert to z scores to determine who did better and by how much. >z scores are standardized, so they can be compared!

14. Comparing Apples and Oranges >If we can standardize the raw scores on two different scales, converting both scores to z scores, we can then compare the scores directly.

15. Transforming z Scores into Percentiles >z scores tell you where a value fits into a normal distribution. >Based on the normal distribution, there are rules about where scores with a z value will fall, and how it will relate to a percentile rank. >You can use the area under the normal curve to calculate percentiles for any score.

16. The Normal Curve and Percentages

17. Check Your Learning  If the mean is 10 and the standard deviation is 2: If a student’s score is 8, what is z? If a student’s scores at the 84th percentile, what is her raw score? z score? Would you expect someone to have a score of 20?

18. The Central Limit Theorem >Distribution of sample means is normally distributed even when the population from which it was drawn is not normal! >A distribution of means is less variable than a distribution of individual scores.

19. Creating a Distribution of Scores These distributions were obtained by drawing from the same population.

21. >Mean of the distribution tends to be the mean of the population. >Standard deviation of the distribution tends to be less than the standard deviation of the population. The standard error: standard deviation of the distribution of means Distribution of Means

22. Using the Appropriate Measure of Spread

24. The Mathematical Magic of Large Samples

25. The Normal Curve and Catching Cheaters >This pattern is an indication that researchers might be manipulating their analyses to push their z statistics beyond the cutoffs.

26. Check Your Learning  We typically are not interested in only the sample on which our study is based. How can we use the sample data to talk about the population?