1. The Normal Curve and Sampling Error
Chapter 6
The Normal Curve and Sampling Error

2. Difference between SD & SE?
SD is the sum of the squared deviations from the mean.
SE is the amount of ERROR in the estimate of the population based on the sample.

3. Need for Standard Scores
In the Olympics the last place finishers are all superior to non-Olympic athletes.
How good is a 4 m long jump in high school?
Given mean = 5, we know that 4 m was below the mean.

4. More than 95% of scores fall between ± 2 SD Z of 1.96 is the 95% value

5. Z Scores

6. Computing Confidence Intervals

7. Area Under Normal Curve
Z = 1.96 is 95% level.
2.5% in each tail.
= 47.50

10. T Score

13. Skewed Distribution

14. Interpretation of Skew
Skew is acceptable as long as the Z score is less than 2.0 [p 89]

15. Interpretation of Kurtosis
Kurtosis is acceptable as long as the Z score is less than 2.0 [p 89]

16. SPSS Interpretation of Skew From Table 6.2 p 87
A skew that is more than twice it’s SE is taken as a departure from symmetry.
In this case the Skew of is not greater than
2 * .637 = 1.274
See p 287 of SPSS Base Manual

18. Confidence Intervals

19. Using SE for Confidence Interval

20. Computing Confidence Intervals