OUTLINE FOR CHAPTER
6
Estimation
 Introduction
 The two main kinds of estimation: interval estimation &
point estimation
 Estimation (interval or point) compared with the second main form
of inferential statistics: hypothesis testing
 Interval Estimation
 Sampling Error
 What a sampling error is . . . and what it isn't?
 Should we expect sampling error when a random
sample is drawn?
 Sampling Distributions and Standard Errors
 Sampling distribution . . . a frequency distribution showing
the results of multiple random samples of the same size drawn
from the same population
 Standard error . . . the standard deviation of a sampling
distribution
 Estimating the standard error from a single sample
 Estimate standard errors for different things: mean, proportion,
r, etc.
 The abbreviation SEM
 Reporting estimated standard errors in passages of text, tables,
and graphs
 Confidence Intervals
 What they look like (and how they appear in research reports)
 How they're constructed? . . . and 4 things that affect the
interval's width?
 How they ought to be interpreted?
 Their advantage over estimated standard errors
 Point Estimation
 How does one do point estimation?
 The big problem with point estimation: it's likely to fail due
to sampling error
 Places where point estimation is used by researchers:
 Reliability and validity coefficients
 As a core ingredient when building confidence intervals
 Warning Concerning Interval and Point Estimation
 If two numbers are separated by a "±" symbol, the number
that comes after the "±" symbol might be a SD . . . or it
might be an estimated standard error
 Standard errors are not determined, they're only
estimated
 Sometimes (as when dealing with r), the sample statistic will
not be located exactly halfway between the end points
of the confidence interval
 Neither interval estimation nor point estimation can be expected
to work if based upon nonrandom samples
