1. The Population vs. The Sample
We will likely never know these (population parameters - these are things that we want to know about in the population)
The population
Number = N
Mean = m
Standard deviation = s
Cannot afford to measure parameters of the whole population

2. 3 General Kinds of Sampling
Haphazard sampling
Based on convenience and/or self-selection
Street-corner interview, mall intercept interview
Television call-in surveys, questionnaires published in newspapers, magazines, or online
Literary Digest poll (2 million) versus George Gallup poll (2,000) before the 1936 election

3. 3 General Kinds of Sampling
Quota sampling
Categories and proportions in the population
More representative than quota sampling
Interviewers have too much discretion
Probability sampling
A sample of a population in which each person has a known chance of being selected
Basically an equal chance at the start

4. Size of a Probability Sample
Depends on:
Accuracy (margin of error) typically +/-3%
Confidence level: probability that the results are outside the specified level of accuracy
Variability: researchers usually assume maximum variability for a binomial variable
Random sampling
Multistage cluster sampling

5. The Population vs. The Sample
We will likely never know these (population parameters - these are things that we want to know about in the population)
The population
Number = N
Mean = m
Standard deviation = s
Cannot afford to measure parameters of the whole population
So we draw a random sample.

6. The Population vs. The Sample
The sample Sample size = n Sample mean = x Sample standard deviation = s
Cannot afford to measure parameters of the whole population
So we draw a random sample.

7. The Population vs. The Sample
Does m = x? Probably not. We need to be confident that x does a good job of representing m.
The population
Number = N
Mean = m
Standard deviation = s
The sample Sample size = n Sample mean = x Sample standard deviation = s

8. Connecting the Population Mean to the Sample Mean
How closely does our sample mean resemble the population mean (a “population parameter” in which we are ultimately interested)?
Population parameter = sample statistic +
random sampling error
(or “standard error”)
Random sampling error = (variation component) . or “standard error” (sample size component)
Use a square-root function of sample size
The sample Sample size = n Sample mean = x Sample standard deviation = s
s = measure of variation
Standard error (OR random sampling error) = s Ö (n-1)
Population mean = x s Ö (n-1)
The population mean likely falls within some range around the sample mean—plus or minus a standard error or so.

9. To Compute Standard Deviation
Population standard deviation
Sample standard deviation

10. Why Use Squared Deviations?
Why not just use differences?
Student A’s exam scores/(Stock A’s prices):
94, 86, 94, 86
Why not just use absolute values?
Student B’s exam scores/(Stock B’s prices):
97, 84, 91, 88
Which one is more spread out /unstable /risky /volatile?

12. is the formula for:
Population standard deviation
Sample standard deviation
Standard error
Random sampling error
Population mean