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

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25 Nov 2012

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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 estimate—for 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