Plotting the Wilson distribution

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1 , Survey of English Usage, University College London Setember Introduction We have discussed the Wilson score interval at length elsewhere (Wallis 013a, b). Given an observed Binomial roortion = f / n observations, and confidence level 1-α, the interval reresents the two-tailed range of values where P, the true roortion in the oulation, is likely to be found. Note that f and n are integers, so whereas P is a robability, is a roer fraction (a rational number). The interval rovides a robust method (Newcombe 1998, Wallis 013a) for directly estimating confidence intervals on these simle observations. It can take a correction for continuity in circumstances where it is desired to erform a more conservative test and err on the side of caution. We have also shown how it can be emloyed in logistic regression (Wallis 015). The oint of this aer is to exlore methods for comuting Wilson distributions, i.e. the analogue of the Normal distribution for this interval. There are at least two good reasons why we might wish to do this. The first is to shed insight onto the erformance of the generating function (formula), interval and distribution itself. Plotting an interval means selecting a single error level α, whereas visualising the distribution allows us to see how the function erforms over the range of ossible values for α, for different values of and n. A second good reason is to counteract the tendency, common in too many resentations of statistics, to resent the Gaussian ( Normal ) distribution as if it were some kind of universal law of data, a mistaken corollary of the Central Limit Theorem. This is articularly unwise in the case of observations of Binomial roortions, which are strictly bounded at 0 and 1. As we shall see, the Wilson distribution diverges from the Gaussian most dramatically as it tends towards the boundaries of the robabilistic range, i.e. where the interval aroaches 0 or 1. By contrast, the Normal distribution is unbounded, and continues to lus or minus infinity. The Wilson score interval (Wilson 197) may be comuted with the following formula. Wilson score interval (w, w + ) z + ± zα n (1 ) zα/ + n 4n α/ / 1 z + α/ n. (1) Let us first consider cases where P is less than. At the lower bound of this interval (P = w ) the uer bound for the Gaussian interval for P, E +, must be equal to (Wallis 013a). We can carry out a test for significant difference between and P by either a) calculating a Gaussian interval at P and testing if is greater than the uer bound, or b) calculating a Wilson interval at and testing if P is less than the lower bound. 1 This aer summarises erformance obtained with a sreadsheet by the author, Exerimenting with different values of and n is recommended.

2 To consider cases where P is greater than, we simly reverse this logic. We test if is smaller than the lower bound of a Gaussian interval for P, or P is greater than the uer bound of the Wilson interval for. The Gaussian version of the test is called the single roortion z test. It can also be calculated as a goodness of fit χ test (Wallis 013a, b).. Plotting the distribution We can define the Wilson distribution as follows: the distribution of the redicted robability of the true value P, based on an observation, where P has a known relationshi to, comuted using the Wilson score interval. More recisely, we might consider it as the sum of two distributions: the distribution of the Wilson score interval lower bound w, based on an observation and the distribution of the Wilson score interval uer bound w +..1 Obtaining values of w First, we calculate the lower bound w from Equation (1) above for a series of values of α. In ractice, we obtain a reasonably accurate initial lot by comuting z α/ and thus w, for α A where A = {00, 5,, , 1}, i.e. for intervals of 5 but excluding zero. w (α) = zα/ + zα n / (1 ) + n z α/ 4n 1 + z α/ n. () Note that z α/ for α = 1, z 1/ = 0, and for α > 1, z α/ = z (1 α/) and w (α/) = w + (1 α/). For α > 1, calculating w comutes ercentage oints above the observation (i.e. w + ). So to comute w + we can simly extend A beyond 1, to include {1, , 1.95, }. By insection we note that the limit region below 5 (and above 1.95), is likely to see gradient change as α aroaches zero. In other words, we cannot assume the line between these oints is a straight line. Therefore we add oints to A covering successive fractions {1/40, 1/80, 1/640}, and { 1/40, 1/80, 1/640}. Equation () obtains a osition on a horizontal robability scale, w, comuted for a given cumulative robability α. In other words, for w <, the formula tells us that there is a robability of α that the true value is below w. area δ h error e. Emloying a delta aroximation The next stage is to convert this cumulative robability into a column height. To do this we emloy a delta aroximation, a trick familiar to students of calculus. area α δ The simlest method is to calculate Equation () for two oints, α and α δ. We aroximate the area between the resulting values of w to a column δ wide and h high, we can comute h = width / area, which we can lot over w. w (α δ) w (α) Figure 1. Estimating the height of w (α), h(α), using a onse-sided delta aroximation. As δ 0, error e 0. Coyright 018. All rights reserved.

3 To lot w for areas below, α < 1, we can use the following formula. h(α) = 0 if w ( α) = w ( α δ) δ = w ( α) w ( α δ) otherwise. (3) The first test deals with cases where = 0, which obtain a situation where all values of w (α) = 0. We can continue this aroximation for α 1. But if we want symmetric results for =, we can take a delta above α for all cases for α 1. h(α) = 0 if w ( α) = w ( α + δ) δ = w ( α + δ) w ( α) otherwise. (4) Finally, we set h(1) = 0 when = 0 or 1. Equation (3) converges to the correct value as δ 0. It follows that δ should be as small as ossible. By exerimentation, we find that if δ is below 001, in some versions of Excel results become unreliable. Aroximations in the comutation of z α/ seem to be the culrit. This leaves us with a small error in the calculation. We can see this error in that Equations (3) and (4) do not obtain exactly the same results. At the scale of the grah, this error is small, but erceivable. To minimise this error, we average heights estimated using delta aroximations above and below α. This imroves the estimate for any monotonic region (α δ, α + δ), and does not substantially worsen it if α reresents a eak value. h(α) = ½ ( w δ + ( α) w ( α δ) w δ ). (5) ( α + δ) w ( α) Although the distribution may be comuted with a single formula over α (0, ), recall that the Wilson distribution is really the sum of two distributions, each with a unit area of 1. The first of these areas is the distribution for the uer bound w +, the second the distribution for the lower bound w. (This distinction will become imortant later on.) To scale these distributions to the same scale as the equivalent Binomial or Normal distribution above and below, we can divide both uer and lower bound distributions by n. 3. Examle lots 3.1 An initial examle To begin with, let us hold n = 10. This is a small samle size, but not so small as to resent articular issues. First, we will consider =. We obtain an interval that aears at first sight to match the Normal distribution. A region where the gradient is always increasing or decreasing, i.e. everywhere excet where the eak value is within the range. Coyright 018. All rights reserved. 3

4 For the uroses of comarison we have also lotted Normal distributions centred at P = w (5) and w + (5), divided by n. These distributions therefore have the same area (ignoring boundary cliing) as each corresonding area of the Wilson distribution below and above. =, n = 10, α = 5 distribution of w distribution of w + 0. w (α) w + (α) W+ N+ T t Figure. Plot of Wilson distribution (centre), with tail areas highlighted for α = 5, lotted =, n = 10; with Normal distributions centred at w and w Proerties of the Wilson distributions In this figure, the area under the Wilson distribution for w + (where > ), W+, has the same area as the area under the comlementary Normal distribution N+ (assuming that the Normal distribution is unclied). In this case, area(w+) = area(n+) = 1/n. It also has the same area as the Wilson distribution for w. Provided that (0, 1) (i.e. it is not at the extremes), the interval will be two-sided, area(w+) = area(w ) and have a total area of area(w+) = /n. The tail areas of the Wilson distributions reresent 5 of the area under the curve above and below resectively, in the same way as the equivalent tail areas of the Normal distribution. The tail areas of both distributions on either side of are also 5 of the area under those curves above and below these centres. For small n, the Normal distribution is visibly clied by the robability range, but we can disregard the clied section of these distributions for testing uroses, as our observation is always on the inner side of these distributions. The tail areas for the Normal, area(t) = area(n+) α/. Both tail areas for the Wilson interval, below w (α) and above w + (α), are α = 5 of each searate distribution. Thus in Figure, area(t) = area(w ) α (i.e., α/ of the total area). This obtains a two-tailed test when is not at the extremes, but a one-tailed test when is at 0 or 1. Coyright 018. All rights reserved. 4

5 3.3 Varying As tends to 0, we obtain increasingly skewed distributions (Figure 3). The interval cannot be easily aroximated by a Normal interval, and the sum of the two distributions is decidedly not Gaussian ( Normal ). In Figure 3, note how the mean is no longer the most likely value (mode). In lotting this distribution air, the area on either side of is rojected to be of equal size, i.e. it treats as a given that the true value P is equally likely to be above and below. This is not necessarily true! Indeed we might multily both distributions by the robability of the rior. But this fact should not cause us to change the lot. =, n = 10, α = 5 w 0. w =, n = 10, α = 5 Note how, thanks to the roximity to the boundary at zero, the interval for w becomes increasingly comressed between 0 and, reflected by the increased height of the curve. 0.6 w The tendency to exress the distribution like an exonential decline on the least bounded side reaches its limit when = 0 or 1. The squeezed interval is uncomutable and simly disaears. 3.4 Small n What haens if we reduce n? 0. w + All else being equal we should exect that the smaller the samle size, the larger the confidence interval. In the figures that follow we have lotted Wilson distributions for = 0 and = for n =. Recall also that must be a true fraction of n, so, for examle, for n =, = 0. would not be ossible in ractice =, n = 10, α = 5 The interval for α = 5 now sans most of the range between 0 and 1. The boundaries squeeze the interval close to 0 and 1. We obtain the wisdom-tooth shae in Figure 4 and an undulating curve in Figure w + Note that the areas are larger because we are now scaling by / = 1 instead of /10 = 1/ Figure 3. Plots of Wilson distributions for Coyright 018. All rights reserved. =, and. 5

6 =, n =, α = = 0, n =, α = 5 w + 0. w w Figure 4. Plot of Wilson interval for = and n =. With such a large confidence interval, the boundaries at 0 and 1 cause the area to bulge on either side Figure 5. With = 0, and n =, the gradient close to w + (5) is also affected by the boundary at 1, causing the gradient to undulate. 4. Further ersectives on the distribution 4.1 Percentiles of the Wilson distributions We can lot ercentiles of the distributions, as in Figure 6. The set A includes ten-ercentile oints, and we have simly lotted dividing lines to artition the area at each oint. Figure 6 contains two distributions, containing twenty areas in total, each equal in area. distribution of w distribution of w + =, n = % lower bound 50% 80% each area = 10% of area above 90% uer bound 10% 10% 10% Figure 6. Ten-ercentiles of the Wilson lower and uer distributions. Each area marked 10% is of equal area. This is not always easy to see, articularly with resect to the tails. Coyright 018. All rights reserved. 6 10%

7 4. The logit Wilson distribution We earlier noted Robert Newcombe s observation (Newcombe, 1998) that save when = 0 or 1 Wilson s score interval is symmetric on a logit scale. Our method for logistic line fitting (regression) uses an estimate of variance based on the Wilson interval exressed on an inverse logistic, or logit scale (Wallis 015). Regression over variance relies on an assumtion that the model of variance emloyed is Normal. In other words, it assumes the logit of the Wilson distribution resembles a Normal distribution. We are now in a osition to exlore that assumtion. We calculate logit(w ) using Equation () and (6): logit() log() log(1 ), (6) where log is the natural logarithm. Figure 7 lots the resulting distribution obtained by delta aroximation, and (for comarison uroses) a closely-matching Gaussian distribution. logit() =, n = logit Wilson Gaussian Figure 7. Logit Wilson distribution, i.e. the Wilson score interval on a logit scale, transformed into a distribution. This closely resembles a Gaussian (Normal) distribution centred on logit(). It turns out that, with the excetion of when is at boundaries 0 or 1 (which we exclude from fitting), the distribution closely matches a Normal distribution estimated by the following. mean µ = logit(), standard deviation σ = (logit() logit(w (α/)) / z α/. (7) Figure 7 shows, by way of comarison, the Normal distribution estimated using α = in this formula. This aroximation imroves with increasing centrality and increasing n. The aroximation is not erfect, but it is considerably less rone to error than aroximating the Normal to the Wilson interval on the robability scale (also known as the Wald interval), or even the generally acceted aroximation of the Normal to the Binomial distribution. Coyright 018. All rights reserved. 7 0

8 4.3 Continuity-corrected Wilson distributions As we noted, the aroximation from the discrete Binomial distribution to the Normal introduces an error that is conventionally mitigated with a continuity correction originally due to Yates (1934). In the case of the Normal distribution around P, this widens the interval by adding /n to the uer bound and subtracting this term from the lower bound. Newcombe (1998) resents a formula for comuting the equivalent Wilson score interval with continuity correction. The equation initially aears forbidding but it includes common terms that can be re-calculated. w 1 n + zα/ { zα/ zα/ n + 4n(1 ) + (4 ) + 1} max( 0, ), and ( n + z ) α/ w + 1 n + zα/ + { zα/ zα/ n + 4n(1 ) (4 ) + 1} min( 1, ). (8) ( n + z ) This is the continuity-corrected version of Equation (1). α/ Earlier we emhasised that the Wilson distribution was really two different distributions: one for w and one for w +. Thanks to the continuity correction, these two formulae do not obtain the same result for α = 1, unlike Equation (1), which converges to a midoint. This means we calculate intervals and heights searately. =, n = 10, α = 5 distribution of w distribution of w + 0. w (5) w + (5) ± /n standard Wilson continuitycorrected Figure 8. Uncorrected Wilson distribution (solid line) with continuity-corrected distributions for uer and lower bounds (dashed). We can see the effect of the continuity correction on the intervals, rendering them more conservative (moving them further out from ), at the same time as causing the interval to be comressed even further within the robabilistic range [0, 1]. Coyright 018. All rights reserved. 8

9 Conclusions The Wilson score interval is a member of a class of confidence intervals that correctly characterise exected variation about an observation of a Binomial roortion, [0, 1]. These intervals include the Cloer-Pearson interval, calculated by finding roots of the Binomial distribution for a given α, and the Wilson interval with continuity-correction that we document here. All three behave similarly, with the Cloer-Pearson falling between the two Wilson interval distributions deicted in Figure 8. See Newcombe (1998) and Wallis (013a) for a comarison of cometing intervals. Common to this class of intervals is the fact that they are affected by boundary conditions at 0 and 1. In discussing the logistic curve, Wallis (010) ointed out that the inverse logistic or logit function mas a robabilistic range to an unbounded Real dimension y by effectively folding sace as it aroaches the boundary. Figure 9 shows the idea. 3 logit() Figure 9. Absolute logit cross-section folding an infinite lane into a robabilistic trench. After Wallis (010). It is this folding of the interval into robability sace that exlains two asects of the Wilson distribution we observe. 1. As aroaches 0 or 1, the distribution between the boundary and becomes increasingly comressed and is ushed u, in some cases above the distribution at. Meanwhile the interval on the oen side increasingly resembles a decay curve. This exlains the shae of the distributions in Figure 3.. In Figure 4 and 5, we examined what haens to the distribution for small n. This aeared to generate what at first sight seems an even more baffling result, namely that for = and n =, the distribution had two eaks (it was bimodal ). A small n causes the distribution to sread over most of the robability range. The boundaries distort what would otherwise be a declining interval. We see a similar but less dramatic effect for = 0. The logit transformation of the same interval for = and n = obtains a bell curve aroximating to a Normal distribution about 0. We showed that rovided that was not at 0 or 1, not only is the logit Wilson interval symmetric as Newcombe (1998) ointed out, it resembles a Normal distribution. With increased n, the aroximation imroves, and for n = 10 the aroximation is very close indeed (see Figure 7). This distribution is centred at logit(), with a standard deviation that may be obtained from the width of the Wilson interval on a logit scale. This observation is suort for the generalised logistic regression method described in Wallis (015). Our final comment relates to a oint we made by way of introduction. It is often imortant to lot distributions to hel us concetualise the erformance of what otherwise may aear to be Coyright 018. All rights reserved. 9

10 dry algebraic functions. Statistical distributions are not exerienced directly. They reresent the aggregated sum of exeriences, and statistical reasoning is necessarily an act of imagination. The bell curve exectation is the ideological redisosition to exect that variation around observations of any kind is Normal and symmetric. This exectation aears in the Wald interval or resentations of standard error for observed roortions or robabilities. As we have shown, the redicted distribution of future observations based on a single observation of a Binomial roortion cannot be Normal. Where the observation is suorted by a large n and the distribution is tightly sread, and/or where the observation is close to, the distribution may be aroximately Normal. But many tyes of data are highly skewed, and there are often good reasons why we might wish to work with small n. In the 1990s, medical statisticians started aying attention to this question. Consider a clinical trial for a new heart drug for atients vulnerable to heart attacks. We have an exected rate of heart attacks for this grou based on revious clinical data. We do not wish to recruit more subjects than necessary, so we must work with small n. The exected chance of a heart attack, P, over a short monitored eriod, t, is still small however, being close to zero. A clinical trial manager must contend with two questions. 1. How many heart attack incidents would be significantly greater than would be exected by chance? In other words, does the lower bound of observed rate of heart attacks in the subject grou,, at a given time t exceed P sufficiently to be incaable of being exlained by chance? The trial should sto immediately because the drug aears to be having a negative effect.. Following a trial eriod, is the drug working so well that further trials may be accelerated, more subjects recruited, etc.? To reach this conclusion we must examine the uer bound of our observed heart attack rate, / t. Either way, we are concerned with robabilities that are likely to be close to, but not equal to, zero, by observing roortions of events found in small samles. We need an accurate method for identifying when either stoing condition is reached without extending t longer than necessary. This is what the Wilson class of intervals obtains. References Newcombe, R.G Two-sided confidence intervals for the single roortion: comarison of seven methods. Statistics in Medicine 17: Wallis, S.A Cometition between choices over time. London: Survey of English Usage. htt://corlingstats.wordress.com/01/03/31/cometition-between-choices-over-time Wallis, S.A. 013a. Binomial confidence intervals and contingency tests: mathematical fundamentals and the evaluation of alternative methods. Journal of Quantitative Linguistics 0:3, Wallis, S.A. 013b. z-squared: the origin and alication of χ². Journal of Quantitative Linguistics 0:4, Wallis, S.A Logistic regression with Wilson intervals. London: Survey of English Usage. htt://corlingstats.wordress.com/015/04/4/logistic-regression Wilson, E.B Probable inference, the law of succession, and statistical inference. Journal of the American Statistical Association : Yates, F Contingency tables involving small numbers and the chi-square test. Journal of the Royal Statistical Society, 1: Coyright 018. All rights reserved. 10

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