KEY TERMS

Confidence Interval (CI)
an interval estimate for an unknown population parameter. This depends on:

• the desired confidence level,
• information that is known about the distribution (for example, known standard deviation),
• the sample and its size.

Confidence Level (CL)
the percent expression for the probability that the confidence interval contains the true population parameter; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter.

Degrees of Freedom (df)
the number of objects in a sample that are free to vary

Error Bound for a Population Mean (EBM)
the margin of error; depends on the confidence level, sample size, and known or estimated population standard deviation.

Error Bound for a Population Proportion (EBP)
the margin of error; depends on the confidence level, the sample size, and the estimated (from the sample) proportion of successes.

Inferential Statistics
also called statistical inference or inductive statistics; this facet of statistics deals with estimating a population parameter based on a sample statistic. For example, if four out of the 100 calculators sampled are defective we might infer that four percent of the production is defective.

Parameter
a numerical characteristic of a population

Point Estimate
a single number computed from a sample and used to estimate a population parameter

Student’s t-Distribution
investigated and reported by William S. Gossett in 1908 and published under the pseudonym Student; the major characteristics of the random variable (RV) are:

• It is continuous and assumes any real values.
• The pdf is symmetrical about its mean of zero. However, it is more spread out and flatter at the apex than the normal distribution.
• It approaches the standard normal distribution as n get larger.
• There is a “family” of t–distributions: each representative of the family is completely defined by the number of degrees of freedom, which is one less than the number of data.