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1. normal distributions- studied by France’s de Moivre & Germany’s Gauss to provide
foundation of statistics
2. normal curves- bell curved graph of a normal distribution
3. upward cup on normal curve- less than x –  or x + 
4. downward cup on normal curve- between x –  or x + 
5. symmetry of normal curves- same on both sides of the mean
6. empirical rule- 68%, 95%, 99.7%
7. z value or z score- the number of standard deviations between x and  with . z = (x-)/
8. standard units- mean = 0, greater than the mean = > 0, less than the mean = < 0
9. standard normal distribution m = 0 and  = 1, 68% between -1 & 1, 95% between -2 & 2,
99.7% between -3 & 3
10. raw score, x = z + 
11. area under the standard normal curve = probability of any item that falls within an interval
12. population parameter- numerical measure of a population…mean (), variance (2), standard
deviation ) , and proportion (p)
13. statistic - numerical measure of a sample…mean (x̄ ), variance (s2), standard deviation
(s) and proportion (p̂ or p-hat)
14. sampling distribution- probability distribution of sample statistic based on same size
from same population
x = mean of a sampling distribution = population mean 
x = standard error of the mean = /√n
17. standard error of the mean = /√n
18. central limit theorem- z = (x-)/ (/n) when n >30
19. normal approximation to the binominal distribution- n trials, r successes, p probability,
q = 1-p, np> 5, nq > 5,  = np, and  = √(npq)
20. continuity correction- left point -0.5, right point +0.5
21. average- sample mean x̄ or population mean 
22. estimation- value of a parameter
23. testing- decision about a parameter
24. regression- prediction about a statistic