PSYC 210 Chapter Notes - Chapter 7: Statistical Parameter, Squared Deviations From The Mean, Standard Score

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Chapter 7 Vocabulary 1
t test hypothesis-testing procedure in which the population variance is unknown; it compares t scores from a
sample to a comparison distribution called a t distribution
t test for a single sample hypothesis-testing procedure in which a sample mean is being compared to a known
population mean and the population variance is unknown
Biased estimate estimate of a population parameter that is likely systematically to overestimate or
underestimate the true value of the population parameter; for example, would be a biased estimate of the
population variance (systematically underestimate it)
Unbiased estimate of the population variance () estimate of the population variance, based on sample scores,
which has been corrected so that it is equally likely to overestimate or underestimate the true population variance;
the correction used is dividing the sum of squared deviations by  , instead of the usual procedure of dividing
by the sample size directly
Degrees of freedom (df) number of scores free to vary when estimating a population parameter; usually part of a
formula for making that estimatefor example, in the formula for estimating the population variance from a
single sample, the degrees of freedom is the number of scores minus 1
t distribution mathematically defined curve that is the comparison distribution used in a t test
t table table of cutoff scores on the t distribution for various degrees of freedom, significance levels, and one-
and two-tailed tests
t score on a t distribution, number of standard deviations from the mean (like a Z score but on a t distribution)
Assumption condition, such as a population’s having a normal distribution, required for carrying out a particular
hypothesis-testing procedure; a part of the mathematical foundation for the accuracy of the tables used in
determining cutoff scores
Robustness extent to which a particular hypothesis-testing procedure is reasonably accurate even when its
assumption are violated
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