8/26/10
Class Notes: The Normal Distribution
Objective:
To understand the normal distribution and the ways we will use it to make inferences from our data
Characteristics of Normal Distributions:
Unimodal, symmetrical, bellshaped
Theoretical distributions, not empirical distributions
1.
Based on logic and mathematics (not observations)
2.
A normal distribution never drops to baseline at either end
3.
Normal distributions are very valuable
Theoretically the area between the curve and the baseline is taken to be 100%
Importance of Normal Distributions:
Measurements of many naturally occurring phenomena (intelligence, height, aggressiveness, etc)
The sampling distribution of the means
The Normal Distribution Table (or the ztable) and z scores
Because the normal distributions are exactly defined, it is possible to compute
the exact percentage of
the scores that will fall between any two points in the distribution (or to compute the exact probability
that a score taken at random will fall between any two points in the distribution.
That is, it is possible to
determine the exact percentage of cases between any two z scores.
Getting to Know the z table (do this well!)
Probability x 100 = %
Find percentage of scores below (or above) a specific raw score using the z table
1.
Draw a distribution
2.
Convert the raw score into a z score
3.
Look up the z table, find the z score, and then see the corresponding p
4.
Multiply by 100
Find the percentage of scores that falls BETWEEN two raw scores?
1.
Draw a distribution
2.
Convert raw scores into z scores
3.
Look up the z table, find the z scores and then find appropriate probability
4.
Add or subtract to get at total probability
5.
Multiply by 100
Find the raw score that represents a certain cutoff probability
1.
Draw the curve
2.
Look up the normal curve table and find the closest %
3.
Find the z score in the z column
4.
Convert the z score into a raw score
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Class Notes: Sampling
Objective:
To identify differences between samples and populations; to understand what makes a good sample
and what samples represent
Why do we study samples instead of populations?
Review of Definitions:
Population:
Sample:
M
Methods of Sampling
*****The ability to generalize from a sample to the population depends on the REPRESENTATIVENESS of
the sample****
Random sampling: Every element of the population being studied has an equal probability of being included in
the sample
Random Sampling vs. Random Assignment
Review of Definitions:
Population:
Sample:
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 Fall '10
 VANDELLEN
 Normal Distribution, Standard Deviation, 1%, 5%, 10 hours, 6 inches

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