Gardner, Dalsing, Reyes, and Brake (1984) where Hand F are the ordinates of the standard normal distribution at the points where the tail areas of the distribution are equivalent to the hit rate and the false-alarm rate, respectively. That is, H is the ordinate of the signalplus-noise distribution at the criterion, and F is the ordinate of the noise-alone distribution at the criterion. Gescheider (1985, chap. 4) and Hochhaus (1972) concisely described computation of {3.To test the accuracy of the Gardner et al. (1984) The magnitude of {3 indicates the stringency of the criterion an observer uses in deciding to report detection of a signal. High {3 signifies a strict criterion; low {3 signifies a lax criterion. {3 can be determined from the relationship between (1) the proportion of trials in which the observer reports a signal when the signal is present (the hit rate) and (2) the proportion of trials in which the observer reports a signal when the signal is absent (the falsealarm rate). Values of {3 are calculated by the equationrors occur primarily at low levels of the false-alarm rate, but every page of the table contains some errors. ALTERNATIVES TO THE TABLEAlthough the errors in the Gardner et al, (1984)

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Procedures are described for setting confidence intervals for a true score and a retest score given an obtained score. The confidence intervals are functions of the estimated true score (not the obtained score) and either the standard error of the true score or the standard error of estimate of the retest score. Errors that result from the customary but incorrect use of confidence intervals are analyzed for IQ scores on the Wechsler Intelligence Scale for Children‐Revised and the Wechsler Adult Intelligence Scale‐Revised. Simple equations are given to establish confidence intervals for scores on any test.

In statistics programs, the evaluation of a normal distribution function is usually accomplished by an approximation algorithm, the most popular of which are those based on Hastings (1955) and adapted by Zelen and Severo (1964). This paper compares seven approximations of the normal distribution function with respect to accuracy and speed of execution on a microcomputer. The results can aid in choosing an appropriate procedure for estimating the probability of a normal deviate (z). 17 from Zelen and Severo require the calculation of the probability density as a first step. Cadwell's approximation was modified slightly by substitution of a new correction term for two terms in the original formula. The modification not only simplifies the approximation but also improves its accuracy somewhat for many values of z. Moran's approximations, which have not yet received much attention, were programmed from Moran's Equations 4 and 5. Unlike Hastings-type approximations, Cadwell's and Moran's approximations do not rely on special constants, although they do use pi, and Moran's approximations use the sine function.Each approximation returns the one-tailed probability of the value of z that is entered. A positive z is assumed as input for all the approximations except Cadwell's (1951), but Line 60 of the driver program, which calculates the percentile rank of z, illustrates how the sign of z can be taken into account.Tests of the Approximations. The approximations were tested on a Radio Shack Color Computer. This machine was selected because it has nine-digit numeric precision and because it is a relatively popular, inexpensive computer that is useful in the psychology laboratory