The Geometric Mean: Confidence Limits and Significance Tests

Perception & Psychophysics 1979, Vol. 26 (5), 419-421

The geometric mean: Confidence APPROXIMATING THE STANDARD ERROR limits and significance tests Derivation EDWARD F. ALF and JOHN M. GROSSBERG Consider a sample of n values, x., Xl' ••• , Xi, San Diego State University ••• ,Xn , from the random variable X, in which San Diego, California 92182 Xi > 0 for all population values. The sample geo metric mean, G, is defined as: The variability of magnitude estimations has been found to grow approximately in proportion to the magnitude, (1) and to produce distributions that are roughly log normal. Consequently, averaging is done best by taking geometric From Equation 1, the arithmetic mean of the loga means of the estimations. This method of averaging also rithms of the X values, M, may be written as: has the advantage that, despite the different ranges of numbers used by different observers, no normalizing is needed prior to averaging. (Stevens, 1966, p. 531) M = (Llnx)/n = In G. (2)

Ernst Heinrich Weber (179~-1878) is generally That is, the logarithm of the geometric mean, lnG, credited with being the first to observe that the incre is equal to M; the arithmetic mean of the logarithms ment in a stimulus that is just noticed is proportional of the sample values. It is sometimes more con to the size of the stimulus. According to Boring venient to calculate G as the antilogarithm of the (1957), Weber performed many experiments verify mean of the logarithms: ing this, and reported them in his Latin monograph, De tactu: annotationes anatomicae et physiologicae, G = f(M) = eM. (3) which appeared in 1834. Weber noted, for example, that, in lifting weights, we can "just perceive" the We may define the parameter /-I as: difference between two weights if they differ by about lI40th, e.g., if the heavier is lI40th larger /-I = E(M). (4) than the lighter of the two weights. The heavier the weight, the greater must be the increment in order Note that /-I is the population arithmetic mean, or for it to be noticed. expected value, of the random variable InX. Although just perceptible increments change as a We may define a second parameter, /-Ie, as an function of stimulus size, the difference between the explicit function of /-I: logarithms of the stimulus and the just noticeably larger stimulus will be constant. Thus, to the extent /-Ie = frange. Furthermore, these logarithms variable X. And when the distribution of InX is tend to be normally distributed, and tend to have symmetrical, /-Ie will also be the population median the same standard deviation for different stimuli of the random variable X. throughout the stimulus range. As the opening quo Any sample geometric mean, G, may be expressed tation from Stevens (1966) indicates, it can be argued in the form that in combining several independent estimates of a judged quantity, one should find the mean of the G = /-Ie + AG = fstatistic: (1) What is the for mula for finding the approximate standard error (7) of the geometric mean? (2) How can we calculate the exact confidence limits for the geometric mean? where f' (/-I) indicates that the first derivative is to be The present paper provides answers to these ques evaluated at the value /-I. tions. Subtracting Equation 5 from Equation 7,

AG ~ AMf' (J.L). (8) where oM is the variance of the mean of the loga rithms, and 0 2 is the variance of the parent popula Notice in Equation 8 that the errors in G are approxi tion of logarithms from which samples of size n are mately equal to the errors in M multiplied by a con drawn. stant. Therefore, Reasonable estimates of 0 2 may be taken from Luce and Mo (1965, Figure 6, p. 170), which shows (9) the distribution of 2,000 log x values for each of two subjects. From these we would estimate a to fall From Equation 5, in the range .15 ~ a ~ .35. From these estimates and Equation 17, we would estimate oM for samples of ~ ~ fsquare root of Equa lognormal assumption is met. tion 11, The Lognormal Assumption (12) The data of Luce and Mo (1965) suggest that the distribution of magnitude estimates may not he log Equation 12 refers to parameter values. In an normal. This may well be correct. But, as Galton applied problem, one would replace these parameters (1879) suggested in one of the earliest papers on this by their sample estimates, topic, the distribution of M will approach normality as n increases, for all parent distributions to which (13) the central limit theorem applies. Thus, the distribu tion of G will approach the lognormal form, even where G is the sample geometric mean as defined in though the parent distribution of X may not be log Equation 1, and sM is the sample estimate of the normal. standard error of the mean of the logarithms of The approach to normality is usually quite rapid. the sample values, defined as For example, Edwards (1968, p. 78) illustrates how the sampling distribution of the mean of even a com pletely rectangular distribution approaches normality closely in samples as small as n = 4. For unimodal Accuracy of the Approximation leptokurtic distributions like those in Figure 6 of The approximation in Equation 11 doesn't require Luce and Mo (1965), the approach to normality the random variable X to be lognormally distributed, should be even more rapid. To check on this assump but its accuracy does depend on the degree to which tion, we constructed hypothetical distributions simi the relation between G and M is approximately lin lar to those in Figure 6 of Luce and Mo, and the ear. And the degree of linearity between G and M actual standard errors of the geometric means in is heavily dependent on the value of OM. Let us exam samples of size n = 3 were determined using Monte ine this relation for the lognormal case, using realis Carlo methods. For both subjects, the approxima tic values for OM. tion in Equation 11 was closer to the true value than If X is, in fact, lognormally distributed, then the the approximation using the square root of Equa variance of G is exactly tion 15. Equation 11 overestimated 00 by 3.03070 and 4.9070 for subjects 11 and 8, respectively; while the (15) square root of Equation 15 overestimated 00 by 4.65070 and 6.9070, respectively. The slightly greater (see, e.g., Hastings & Peacock, 1975, p. 84). accuracy of Equation 11 may be due to its fewer From Equations 11 and 15, the percent error in assumptions regarding the parent distribution; so Equation 11 may be expressed as it may be preferable when the lognormal assumption is questionable. In any case, both approximations 0J0 error = l00{[J.L~oM]/[J.L~eaM(eaM - I)]}. (16) become more accurate as sample size increases.

Finally,we may express oM in the form Computational Procedures Let x be a subject's judgment, and assume we have (17) n values of x for which we wish to find the geometric NOTES AND COMMENT 421 mean and its standard error. The computational pro and the lower limit is cedures are as follows: (1) Find the natural logarithm, In x, for each x value. (2) Find the mean of the (20) logarithms, which is simply the sum of the In x values :divided by n, the number of such values. The limits in Equations 19 and 20 are for the expected (3) Find the antilogarithm of the mean of the loga value of the mean of the logarithms. Since the geo rithms. This is the geometric mean. (4) Find the stan metric mean is a monotonic function of the mean dard error of the mean of the logarithms. This is of the logarithms, the upper and lower confidence given by Equation 14. (5) Multiply the standard limits for the geometric mean are error of the mean of the logarithms by the geometric mean. The resulting product is the standard error of Upper Limit = eU, (21) the geometric mean. Lower Limit = eL. (22)

EXACT METHODS Significance Tests For hypotheses regarding the difference between The methods of the preceding sections are approxi geometric means, it is possible to perform conven mate, but they should be serviceable enough for most tional t tests on the logarithms of the observations. purposes. However, when the assumptions of Stevens Since the geometric mean is a monotonic function of (1966) are met, and the responses follow a log normal the mean of the logarithms, significance of the dif distribution, then exact significance tests and exact ference between the means of the logarithms implies confidence limits can be obtained for the logarithms significance of the difference between geometric of the responses. Because of the exact monotonic means. relation between the mean of the logarithms and the geometric mean of the responses, it is also possible, REFERENCES under these assumptions, to make exact significance BORING, E. G. A history ofexperimental psychology. New York: tests on the geometric mean. Appleton-Century-Crofts, 1957. EDWARDS, A. L. Experimental design in psychological research Exact Confidence Limits (3rd ed.). New York: Holt, Rinehart and Winston, 1968. Continuing the terminology of the previous sec GALTON, F. The geometric mean in vital and social statistics. tions, if the logarithms of the observations are nor Proceedings ofthe Royal Society ofLondon, 1879,29,365-367. HASTINGS, N. A. J., & PEACOCK, J. B. Statistical distributions. mally distributed, then New York: Wiley, 1975. LUCE, R. D., & Mo, S. S. Magnitude estimation of heaviness and (18) loudness by individual subjects: A test of a probabilistic response theory. British Journal ofMathematical and Statistical Psychol will follow Student's t distribution. ogy, 1965,18,159-174. SCARBOROUGH, J. B. Numerical mathematical analysis (5th ed.). Exact confidence limits can then be found as fol Baltimore: Johns Hopkins Press, 1962. lows. For the (1 - a)OJo confidence limits, the upper STEVENS, S. S. A metric for the social consensus. Science, 1966, limit is 151,530-541. (Received for publication January 30, 1979; (19) revision accepted September 26, 1979.)