--- title: A/B testing long-form readability description: a log of experiments done on the site design, intended to render pages more readable created: 16 Jun 2012 tags: experiments, statistics, computer science, meta, decision theory, shell, R, JS, CSS, power analysis, Bayes status: in progress confidence: possible importance: 4 ... > To gain some statistical & web development experience and to improve my readers' experiences, I have been running a series of CSS A/B tests since June 2012. As expected, most do not show any meaningful difference. # Background - https://www.google.com/analytics/siteopt/exptlist?account=18912926 - http://www.pqinternet.com/196.htm - https://support.google.com/websiteoptimizer/bin/answer.py?hl=en&answer=61203 "Experiment with site-wide changes" - https://support.google.com/websiteoptimizer/bin/answer.py?hl=en&answer=117911 "Working with global headers" - https://support.google.com/websiteoptimizer/bin/answer.py?hl=en-GB&answer=61427 - https://support.google.com/websiteoptimizer/bin/answer.py?hl=en&answer=188090 "Varying page and element styles" - testing with inline CSS overriding the defaults - http://stackoverflow.com/questions/2993199/with-google-website-optimizers-multivariate-testing-can-i-vary-multiple-css-cl - http://www.xemion.com/blog/the-secret-to-painless-google-website-optimizer-70.html - http://stackoverflow.com/tags/google-website-optimizer/hot # Problems with "conversion" metric https://support.google.com/websiteoptimizer/bin/answer.py?hl=en-AU&answer=74345 "Time on page as a conversion goal" - every page converts, by using a timeout (mine is 40 seconds). Problem: dichotomizing a continuous variable into a single binary variable destroys a massive amount of information. This is well-known in the statistical and psychological literature (eg. [MacCallum et al 2002](http://www.psychology.sunysb.edu/attachment/measures/content/maccallum_on_dichotomizing.pdf "On the Practice of Dichotomization of Quantitative Variables")) but I'll illustrate further with some information-theoretical observations. According to my Analytics, the mean reading time (time on page) is 1:47 and the maximum bracket, hit by 1% of viewers, is 1801 seconds, and the range 1-1801 takes <10.8 bits to encode (`log2(1801) ~> 10.81`), hence each page view could be represented by <10.8 bits (less since reading time is so highly skewed). But if we dichotomize, then we learn simply that ~14% of readers will read for 40 seconds, hence each reader carries not 6 bits, nor 1 bit (if 50% read that long) but closer to 2/3 of a bit: ~~~{.R} R> p=0.14; q=1-p; (-p*log2(p) - q*log2(q)) [1] 0.5842 ~~~ This isn't even an efficient dichotomization: we could improve the fractional bit to 1 bit if we could somehow dichotomize at 50% of readers: ~~~{.R} R> p=0.50; q=1-p; (-p*log2(p) - q*log2(q)) [1] 1 ~~~ But unfortunately, simply lowering the timeout will have minimal returns as Analytics also reports that 82% of reader spend 0-10 seconds on pages. So we are stuck with a severe loss. # ideas for testing JS: disqus CSS differences from readability every declaration in default.CSS? Donation placement - left, right, bottom donation text help pay for hosting help sponsor X experiment Xah's text - did you find this article useful? - test the suggestions in https://code.google.com/p/better-web-readability-project/ http://www.vcarrer.com/2009/05/how-we-read-on-web-and-how-can-we.html # Testing ## `max-width` CSS-3 property: set how wide the page will be in pixels if unlimited screen real estate is available. I noticed some people complained that pages were 'too wide' and this made it hard to read, which apparently is a real thing since lines are supposed to fit in eye saccades. So I tossed in 800px, 900px, 1300px, and 1400px to the first A/B test. ~~~{.HTML} ~~~ It ran from mid-June to 1 August 2012. Unfortunately, I cannot be more specific: on 1 August, Google deleted Website Optimizer and told everyone to use 'Experiments' in Google Analytics - and *deleted all my information*. The graph over time, the exact numbers - all gone. So this is from memory. The results were initially very promising: 'conversion' was defined as staying on a page for 40 seconds (I reasoned that this meant someone was actually reading the page), and had a base of around 70% of readers converting. With a few hundred hits, 900px converted at 10-20% more than the default! I was ecstatic. So when it began falling, I was only a little bothered (one had to expect some regression to the mean since the results were too good to be true). But as the hits increased into the low thousands, the effect kept shrinking all the way down to 0.4% improved conversion. At some points, 1300px actually exceeded 900px. The second distressing thing was that Google's estimated chance of a particular intervention beating the default (which I believe is a Bonferroni-corrected _p_-value), did not increase! Even as each version received 20,000 hits, the chance stubbornly bounced around the 70-90% range for 900px and 1300px. This remained true all the way to the bitter end. At the end, each version had racked up 93,000 hits *and still was in the 80% decile*. Wow. Ironically, I was warned at the beginning about both of these possible behaviors by a paper I read on large-scale corporate A/B testing: http://www.exp-platform.com/Documents/puzzlingOutcomesInControlledExperiments.pdf and http://www.exp-platform.com/Documents/controlledExperimentDMKD.pdf and http://www.exp-platform.com/Documents/2013%20controlledExperimentsAtScale.pdf It covered at length how many apparent trends simply evaporated, but it also covered later a peculiar phenomenon where A/B tests did not converge even after being run on ungodly amounts of data because the standard deviations kept changing (the user composition kept shifting and rendering previous data more uncertain). And it's a general phenomenon that even for large correlations, the trend will bounce around a lot before it stabilizes ([Schönbrodt & Perugini 2013](http://www.psy.lmu.de/allg2/download/schoenbrodt/pub/stable_correlations.pdf "At what sample size do correlations stabilize?")). Oy vey! When I discovered Google had deleted my results, I decided to simply switch to 900px. Running a new test would not provide any better answers. ## TODO how about a blue background? see http://www.overcomingbias.com/2010/06/near-far-summary.html for more design ideas 5. table striping ~~~{.Css} tbody tr:hover td { background-color: #f5f5f5;} tbody tr:nth-child(odd) td { background-color: #f9f9f9;} ~~~ 8. link decoration ~~~{.Css} a { color: black; text-decoration: underline;} a { color:#005AF2; text-decoration:none; } ~~~ # Resumption: ABalytics In March 2013, I decided to give A/B testing another whack. Google Analytics Experiment did not seem to have improved and the commercial services continued to charge unacceptable prices, so I gave the Google Analytics custom variable integration approach another trying using [ABalytics](https://github.com/danmaz74/ABalytics). The usual puzzling, debugging, and frustration of combining so many disparate technologies (HTML *and* CSS *and* JS *and* Google Analytics) aside, it seemed to work on my test page. The current downside seems to be that the ABalytics approach may be fragile, and the UI in GA is awful (you have to do the statistics yourself). ## `max-width` redux The test case is to rerun the `max-width` test and finish it. ### Implementation The exact changes: ~~~{.Diff} Sun Mar 17 11:25:39 EDT 2013 gwern@gwern.net * default.html: setup ABalytics a/b testing https://github.com/danmaz74/ABalytics (hope this doesn't break anything...) addfile ./static/js/abalytics.js hunk ./static/js/abalytics.js 1 ... hunk ./static/templates/default.html 28 +
+ ... - + window.onload = function() { + ABalytics.applyHtml(); + }; + hunk ./static/templates/default.html 119 + + ABalytics.init({ + maxwidth: [ + { + name: '800', + "maxwidth_class1": "", + "maxwidth_class2": "" + }, + { + name: '900', + "maxwidth_class1": "", + "maxwidth_class2": "" + }, + { + name: '1100', + "maxwidth_class1": "", + "maxwidth_class2": "" + }, + { + name: '1200', + "maxwidth_class1": "", + "maxwidth_class2": "" + }, + { + name: '1300', + "maxwidth_class1": "", + "maxwidth_class2": "" + }, + { + name: '1400', + "maxwidth_class1": "", + "maxwidth_class2": "" + } + ], + }, _gaq); + ~~~ ### Results I wound up the test on 17 April 2013 with the following results: Width (px) Visits Conversion ---------- ------ ---------- 1100 18,164 14.49% 1300 18,071 14.28% 1200 18,150 13.99% 800 18,599 13.94% 900 18,419 13.78% 1400 18,378 13.68% 109772 14.03% ### Analysis 1100px is close to my original A/B test indicating 1000px was the leading candidate, so that gives me additional confidence, as does the observation that 1300px and 1200px are the other leading candidates. (Curiously, the site conversion average before was 13.88%; perhaps my underlying traffic changed slightly around the time of the test? This would demonstrate why alternatives need to be tested simultaneously.) A quick and dirty R test of 1100px vs 1300px (`prop.test(c(2632,2581),c(18164,18071))`) indicates the difference isn't statistically-significant (at _p_=0.58), and we might want more data; worse, there is no clear linear relation between conversion and width (the plot is erratic, and a linear fit a dismal _p_=0.89): ~~~{.R} rates <- read.csv(stdin(),header=TRUE) Width,N,Rate 1100,18164,0.1449 1300,18071,0.1428 1200,18150,0.1399 800,18599,0.1394 900,18419,0.1378 1400,18378,0.1368 rates$Successes <- rates$N * rates$Rate rates$Successes <- round(rates$Successes,0) rates$Failures <-rates$N - rates$Successes g <- glm(cbind(Successes,Failures) ~ Width, data=rates, family="binomial") ...Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -1.82e+00 4.65e-02 -39.12 <2e-16 Width 5.54e-06 4.10e-05 0.14 0.89 # not much better: rates$Width <- as.factor(rates$Width) rates$Width <- relevel(rates$Width, ref="900") g2 <- glm(cbind(Successes,Failures) ~ Width, data=rates, family="binomial"); summary(g2) ~~~ But I want to move on to the next test and by the same logic it is highly unlikely that the difference between them is large or much in 1300px's favor (the kind of mistake I care about: switching between 2 equivalent choices doesn't matter, missing out on an improvement *does* matter - maximizing β, not minimizing α). ## Fonts The _New York Times_ ran [an informal online experiment](http://opinionator.blogs.nytimes.com/2012/08/08/hear-all-ye-people-hearken-o-earth/ "Hear, All Ye People; Hearken, O Earth (Part One)") with a large number of readers (_n_=60750) and found that the [Baskerville](!Wikipedia) font led to more readers agreeing with a short text passage - this seems plausible enough given their very large sample size and Wikipedia's note that "The refined feeling of the typeface makes it an excellent choice to convey dignity and tradition." ### Power analysis Would this font work its magic on `gwern.net` too? Let's see. The sample size is quite manageable, as over a month I will easily have 60k visits, and they tested 6 fonts, expanding their necessary sample. What sample size do I actually need? Their professor estimates the effect size of Baskerville at 1.5%; I would like my A/B test to have very high statistical power (0.9) and reach more stringent statistical-significance (_p_<0.01) so I can go around and in good conscience tell people to use Baskerville. I already know the average "conversion rate" is ~13%, so I get this power calculation: ~~~{.R} power.prop.test(p1=0.13+0.015, p2=0.13, power=0.90, sig.level=0.01) Two-sample comparison of proportions power calculation n = 15683 p1 = 0.145 p2 = 0.13 sig.level = 0.01 power = 0.9 alternative = two.sided NOTE: n is number in *each* group ~~~ 15000 visitors in each group seems reasonable; at ~16k visitors a week, that suggests a few weeks of testing. Of course I'm testing 4 fonts (see below), but that still fits in the ~2 months I've allotted for this test. ### Implementation I had previously drawn on the NYT experiment for my site design: ~~~{.Css} html { ... font-family: Georgia, "HelveticaNeue-Light", "Helvetica Neue Light", "Helvetica Neue", Helvetica, Arial, "Lucida Grande", garamond, palatino, verdana, sans-serif; } ~~~ I had not used Baskerville but [Georgia](!Wikipedia "Georgia (typeface)") since Georgia seemed similar and was convenient, but we'll fix that now. Besides Baskerville & Georgia, we'll omit [Comic Sans](!Wikipedia) (of course), but we can try [Trebuchet](!Wikipedia "Trebuchet MS") for a total of 4 fonts (falling back to Georgia): ~~~{.HTML} hunk ./static/templates/default.html 28 +
... hunk ./static/templates/default.html 121 + fontfamily: [ + { + name: 'Baskerville', + "fontfamily_class1": "", + "fontfamily_class2": "" + }, + { + name: 'Georgia', + "fontfamily_class1": "", + "fontfamily_class2": "" + }, + { + name: 'Trebuchet', + "fontfamily_class1": "", + "fontfamily_class2": "" + }, + { + name: 'Helvetica', + "fontfamily_class1": "", + "fontfamily_class2": "" + } + ], ~~~ ### Results Running from 14 April 2013 to 16 June 2013: Font Type Visits Conversion ---------- ------ ------- ---------- Trebuchet sans 35,473 13.81% Baskerville serif 36,021 13.73% Helvetica sans 35,656 13.43% Georgia serif 35,833 13.31% sans 71,129 13.62% serif 71,854 13.52% 142,983 13.57% The sample size for each font is 20k higher than I projected due to the enormous popularity of [an analysis of the lifetimes of Google services](/Google-shutdowns) I finished during the test. Regardless, it's clear that the results - with double the total sample size of the NYT experiment, focused on fewer fonts - are disappointing and there seems to be very little difference between fonts. ### Analysis Picking the most extreme difference, between Trebuchet and Georgia, the difference is close to the usual definition of statistical-significance: ~~~{.R} prop.test(c(0.1381*35473,0.1331*35833),c(35473,35833)) # 2-sample test for equality of proportions with continuity correction # # data: c(0.1381 * 35473, 0.1331 * 35833) out of c(35473, 35833) # X-squared = 3.76, df = 1, p-value = 0.0525 # alternative hypothesis: two.sided # 95% confidence interval: # -5.394e-05 1.005e-02 # sample estimates: # prop 1 prop 2 # 0.1381 0.1331 ~~~ Which naturally implies that the much smaller difference between Trebuchet and Baskerville is not statistically-significant: ~~~{.R} prop.test(c(0.1381*35473,0.1373*36021), c(35473,36021)) # 2-sample test for equality of proportions with continuity correction # # data: c(0.1381 * 35473, 0.1373 * 36021) out of c(35473, 36021) # X-squared = 0.0897, df = 1, p-value = 0.7645 # alternative hypothesis: two.sided # 95% confidence interval: # -0.00428 0.00588 ~~~ Since there's only small differences between individual fonts, I wondered if there might be a difference between the two sans-serifs and the two serifs. If we lump the 4 fonts into those 2 categories and look at the small difference in mean conversion rate: ~~~{.R} prop.test(c(0.1362*71129,0.1352*71854), c(71129,71854)) # 2-sample test for equality of proportions with continuity correction # # data: c(0.1362 * 71129, 0.1352 * 71854) out of c(71129, 71854) # X-squared = 0.2963, df = 1, p-value = 0.5862 # alternative hypothesis: two.sided # 95% confidence interval: # -0.002564 0.004564 ~~~ Nothing doing there either. More generally: ~~~{.R} rates <- read.csv(stdin(),header=TRUE) Font,Serif,N,Rate Trebuchet,FALSE,35473,0.1381 Baskerville,TRUE,6021,0.1373 Helvetica,FALSE,35656,0.1343 Georgia,TRUE,5833,0.1331 rates$Successes <- rates$N * rates$Rate rates$Successes <- round(rates$Successes,0) rates$Failures <-rates$N - rates$Successes g <- glm(cbind(Successes,Failures) ~ Font, data=rates, family="binomial"); summary(g) # ...Coefficients: # Estimate Std. Error z value Pr(>|z|) # (Intercept) -1.83745 0.03744 -49.08 <2e-16 # FontGeorgia -0.03692 0.05374 -0.69 0.49 # FontHelvetica -0.02591 0.04053 -0.64 0.52 # FontTrebuchet 0.00634 0.04048 0.16 0.88 ~~~ With essentially no meaningful differences between conversion rates, this suggests that however fonts matter, they don't matter for reading duration. So I feel free to pick the font that appeals to me visually, which is Baskerville. ## Line height I have seen complaints that lines on `gwern.net` are "too closely spaced" or "run together" or "cramped", referring to the [line height](!Wikipedia "Leading") (the CSS property `line-height`). I set the CSS to `line-height: 150%;` to deal with this objection, but this was a simple hack based on rough eyeballing of it, and it was done before I changed the `max-width` and `font-family` settings after the previous testing. So it's worth testing some variants. Most web design guides seem to suggest a safe default of 120%, rather than my current 150%. If we try to test each decile plus one on the outside, that'd give us 110, 120, 130, 140, 150, 160 or 6 options, which combined with the expected small effect, would require an unreasonable sample size (and I have nothing in the pipeline I expect might catch fire like the Google analysis and deliver an excess >50k visits). So I'll try just 120/130/140/150, and schedule a similar block of time as fonts (ending the experiment on 16 August 2013, with presumably >70k datapoints). ### Implementation ~~~{.HTML} hunk ./static/templates/default.html 30 -
+
hunk ./static/templates/default.html 156 - fontfamily: + linewidth: hunk ./static/templates/default.html 158 - name: 'Baskerville', - "fontfamily_class1": "", - "fontfamily_class2": "" + name: 'Line120', + "linewidth_class1": "", + "linewidth_class2": "" hunk ./static/templates/default.html 163 - name: 'Georgia', - "fontfamily_class1": "", - "fontfamily_class2": "" + name: 'Line130', + "linewidth_class1": "", + "linewidth_class2": "" hunk ./static/templates/default.html 168 - name: 'Trebuchet', - "fontfamily_class1": "", - "fontfamily_class2": "" + name: 'Line140', + "linewidth_class1": "", + "linewidth_class2": "" hunk ./static/templates/default.html 173 - name: 'Helvetica', - "fontfamily_class1": "", - "fontfamily_class2": "" + name: 'Line150', + "linewidth_class1": "", + "linewidth_class2": "" ~~~ ### Analysis From 15 June 2013 - 15 August 2013: line % _n_ Conversion % ------ ------- ------------ 130 18,124 15.26 150 17,459 15.22 120 17,773 14.92 140 17,927 14.92 71,283 15.08 Just from looking at the miserably small difference between the most extreme percentages ($15.26 - 14.92 = 0.34$%), we can predict that nothing here was statistically-significant: ~~~{.R} x1 <- 18124; x2 <- 17927; prop.test(c(x1*0.1524, x2*0.1476), c(x1,x2)) # 2-sample test for equality of proportions with continuity correction # # data: c(x1 * 0.1524, x2 * 0.1476) out of c(x1, x2) # X-squared = 1.591, df = 1, p-value = 0.2072 ~~~ I changed the 150% to 130% for the heck of it, even though the difference between 130 and 150 was trivially small: ~~~{.R} rates <- read.csv(stdin(),header=TRUE) Width,N,Rate 130,18124,0.1526 150,17459,0.1522 120,17773,0.1492 140,17927,0.1492 rates$Successes <- rates$N * rates$Rate rates$Successes <- round(rates$Successes,0) rates$Failures <-rates$N - rates$Successes rates$Width <- as.factor(rates$Width) g <- glm(cbind(Successes,Failures) ~ Width, data=rates, family="binomial") # ...Coefficients: # Estimate Std. Error z value Pr(>|z|) # (Intercept) -1.74e+00 2.11e-02 -82.69 <2e-16 # Width130 2.65e-02 2.95e-02 0.90 0.37 # Width140 9.17e-06 2.97e-02 0.00 1.00 # Width150 2.32e-02 2.98e-02 0.78 0.44 ~~~ ## Null test One of the suggestions in the A/B testing papers was to run a "null" A/B test (or "A/A test") where the payload is empty but the A/B testing framework is still measuring conversions etc. By definition, the null hypothesis of "no difference" should be true and at an alpha of 0.05, only 5% of the time would the null tests yield a _p_<0.05 (which is very different from the usual situation). The interest here is that it's possible that something is going wrong in one's A/B setup or in general, and so if one gets a "statistically-significant" result, it may be worthwhile investigating this anomaly. It's easy to switch from the lineheight test to the null test; just rename the variables for Google Analytics, and empty the payloads: ~~~{.HTML} hunk ./static/templates/default.html 30 -
+
hunk ./static/templates/default.html 158 - linewidth: [ + null: [ + ...]] hunk ./static/templates/default.html 160 - name: 'Line120', - "linewidth_class1": "", + name: 'null1', + "null_class1": "", hunk ./static/templates/default.html 165 - { ... - name: 'Line130', - "linewidth_class1": "", - "linewidth_class2": "" - }, - { - name: 'Line140', - "linewidth_class1": "", - "linewidth_class2": "" - }, - { - name: 'Line150', - "linewidth_class1": "", + name: 'null2', + "null_class1": "", + ... } ~~~ Since any difference due to the testing framework should be noticeable, this will be a shorter experiment, from 15 August to 29 August. ### Results While amusingly the first pair of 1k hits resulted in a dramatic 18% vs 14% result, this quickly disappeared into a much more normal-looking set of data: option _n_ conversion ------ ----- ------ null2 7,359 16.23% null1 7,488 15.89% 14,847 16.06% ### Analysis Ah, but can we reject the null hypothesis that ""==""? In a rare victory for null-hypothesis-significance-testing, we do not commit a Type I error: ~~~{.R} x1 <- 7359; x2 <- 7488; prop.test(c(x1*0.1623, x2*0.1589), c(x1,x2)) # 2-sample test for equality of proportions with continuity correction # # data: c(x1 * 0.1623, x2 * 0.1589) out of c(x1, x2) # X-squared = 0.2936, df = 1, p-value = 0.5879 # alternative hypothesis: two.sided # 95% confidence interval: # -0.008547 0.015347 ~~~ But seriously, it is nice to see that ABalytics does not seem to be broken & favoring either option and any results driven by placement in the array of options. ## Text & background color As part of the generally monochromatic color scheme, the background was off-white (grey) and the text was black: ~~~{.Css} html { ... background-color: #FCFCFC; /* off-white */ color: black; ... } ~~~ The hyperlinks, on the other hand, make use of a off-black `color: #303C3C`, partially motivated by Ian Storm Taylor's advice to ["Never Use Black"](http://ianstormtaylor.com/design-tip-never-use-black/). I wonder - should all the text be off-black too? And which combination is best? White/black? Off-white/black? Off-white/off-black? White/off-black? Let's try all 4 combinations here. ### Implementation The usual: ~~~{.HTML} hunk ./static/templates/default.html 30 -
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hunk ./static/templates/default.html 155 - underline: [ + ground: [ hunk ./static/templates/default.html 157 - name: 'underlined', - "underline_class1": "", - "underline_class2": "" + name: 'bw', + "ground_class1": "", + "ground_class2": "" hunk ./static/templates/default.html 162 - name: 'notUnderlined', - "underline_class1": "", - "underline_class2": "" + name: 'obw', + "ground_class1": "", + "ground_class2": "" + }, + { + name: 'bow', + "ground_class1": "", + "ground_class2": "" + }, + { + name: 'obow', + "ground_class1": "", + "ground_class2": "" ... ]] ~~~ ### Data I am a little curious about this one, so I scheduled a full month and half: 10 September - 20 October. Due to far more traffic than anticipated from submissions to Hacker News, I cut it short by 10 days to avoid wasting traffic on a test which was done (a total _n_ of 231,599 was more than enough). The results: Version _n_ Conversion ---- ------ --------- bw 58,237 12.90% obow 58,132 12.62% bow 57,576 12.48% obw 57,654 12.44% ### Analysis ~~~{.R} rates <- read.csv(stdin(),header=TRUE) Black,White,N,Rate TRUE,TRUE,58237,0.1290 FALSE,FALSE,58132,0.1262 TRUE,FALSE,57576,0.1248 FALSE,TRUE,57654,0.1244 rates$Successes <- rates$N * rates$Rate rates$Successes <- round(rates$Successes,0) rates$Failures <-rates$N - rates$Successes g <- glm(cbind(Successes,Failures) ~ Black * White, data=rates, family="binomial") summary(g) # ...Coefficients: # Estimate Std. Error z value Pr(>|z|) # (Intercept) -1.9350 0.0125 -154.93 <2e-16 # BlackTRUE -0.0128 0.0177 -0.72 0.47 # WhiteTRUE -0.0164 0.0178 -0.92 0.36 # BlackTRUE:WhiteTRUE 0.0545 0.0250 2.17 0.03 # # (Dispersion parameter for binomial family taken to be 1) # # Null deviance: 6.8625e+00 on 3 degrees of freedom # Residual deviance: -1.1758e-11 on 0 degrees of freedom # AIC: 50.4 summary(step(g)) # same thing ~~~ So we can estimate the net effect of the 4 possibilities: 1. Black, White: -0.0128 + -0.0164 + 0.0545 = 0.0253 2. Off-black, Off-white: 0 + 0 + 0 = 0 3. Black, Off-white: -0.0128 + 0 + 0 = -0.0128 4. Off-black, White: 0 + -0.0164 + 0 = -0.0164 The results exactly match the data's rankings. So, this suggests a change to the CSS: we switch the default background color from `#FCFCFC` to `white`, while leaving the default `color` its current `black`. Reader Lucas asks in the comment sections whether, since we would expect new visitors to the website to be less likely to read a page in full than a returning visitor (who knows what they're in for & probably wants more), whether including such a variable (which is something Google Analytics does track) might improve the analysis. It's easy to ask GA for "New vs Returning Visitor" so I did: ~~~{.R} rates <- read.csv(stdin(),header=TRUE) Black,White,Type,N,Rate FALSE,TRUE,new,36695,0.1058 FALSE,TRUE,old,21343,0.1565 FALSE,FALSE,new,36997,0.1043 FALSE,FALSE,old,21537,0.1588 TRUE,TRUE,new,36600,0.1073 TRUE,TRUE,old,22274,0.1613 TRUE,FALSE,new,36409,0.1075 TRUE,FALSE,old,21743,0.1507 rates$Successes <- rates$N * rates$Rate rates$Successes <- round(rates$Successes,0) rates$Failures <-rates$N - rates$Successes g <- glm(cbind(Successes,Failures) ~ Black * White + Type, data=rates, family="binomial") summary(g) # Coefficients: # Estimate Std. Error z value Pr(>|z|) # (Intercept) -2.134459 0.013770 -155.01 <2e-16 # BlackTRUE -0.009219 0.017813 -0.52 0.60 # WhiteTRUE 0.000837 0.017798 0.05 0.96 # BlackTRUE:WhiteTRUE 0.034362 0.025092 1.37 0.17 # Typeold 0.448004 0.012603 35.55 <2e-16 ~~~ 1. B/W: $(-0.009219) + 0.000837 + 0.034362 = 0.02598$ 2. $0 + 0 + 0 = 0$ 3. B: $(-0.009219) + 0 + 0 = -0.009219$ 4. W: $0 + 0.000837 + 0 = 0.000837$ And again, 0.02598 > 0.000837. So as one hopes, thank to randomization, adding a missing covariate doesn't change our conclusion. ## List symbol and font-size I make heavy use of unordered lists in articles; for no particular reason, the symbol denoting the start of each entry in a list is the little black square, rather than the more common little circle. I've come to find the little squares a little chunky and ugly, so I want to test that. And I just realized that I never tested font size (just type of font), even though increasing font size one of the most common CSS tweaks around. I don't have any reason to expect an interaction between these two bits of designs, unlike the previous A/B test, but I like the idea of getting more out of my data, so I am doing another factorial design, this time not 2x2 but 3x5. The options: ~~~{.Css} ul { list-style-type: square; } ul { list-style-type: circle; } ul { list-style-type: disc; } html { font-size: 100%; } html { font-size: 105%; } html { font-size: 110%; } html { font-size: 115%; } html { font-size: 120%; } ~~~ ### Implementation A 3x5 design, or 15 possibilities, does get a little bulkier than I'd like: ~~~{.HTML} hunk ./static/templates/default.html 30 -
+
hunk ./static/templates/default.html 146 - ground: [ + ulFontSize: [ hunk ./static/templates/default.html 148 - name: 'bw', - "ground_class1": "", - "ground_class2": "" + name: 's100', + "ulFontSize_class1": "", + "ulFontSize_class2": "" hunk ./static/templates/default.html 153 - name: 'obw', - "ground_class1": "", - "ground_class2": "" + name: 's105', + "ulFontSize_class1": "", + "ulFontSize_class2": "" hunk ./static/templates/default.html 158 - name: 'bow', - "ground_class1": "", - "ground_class2": "" + name: 's110', + "ulFontSize_class1": "", + "ulFontSize_class2": "" hunk ./static/templates/default.html 163 - name: 'obow', - "ground_class1": "", - "ground_class2": "" + name: 's115', + "ulFontSize_class1": "", + "ulFontSize_class2": "" + }, + { + name: 's120', + "ulFontSize_class1": "", + "ulFontSize_class2": "" + }, + { + name: 'c100', + "ulFontSize_class1": "", + "ulFontSize_class2": "" + }, + { + name: 'c105', + "ulFontSize_class1": "", + "ulFontSize_class2": "" + }, + { + name: 'c110', + "ulFontSize_class1": "", + "ulFontSize_class2": "" + }, + { + name: 'c115', + "ulFontSize_class1": "", + "ulFontSize_class2": "" + }, + { + name: 'c120', + "ulFontSize_class1": "", + "ulFontSize_class2": "" + }, + { + name: 'd100', + "ulFontSize_class1": "", + "ulFontSize_class2": "" + }, + { + name: 'd105', + "ulFontSize_class1": "", + "ulFontSize_class2": "" + }, + { + name: 'd110', + "ulFontSize_class1": "", + "ulFontSize_class2": "" + }, + { + name: 'd115', + "ulFontSize_class1": "", + "ulFontSize_class2": "" + }, + { + name: 'd120', + "ulFontSize_class1": "", + "ulFontSize_class2": "" ... ]] ~~~ ### Data I halted the A/B test on 27 October because I was noticing clear damage as compared to my default CSS. The results were: List icon Font zoom _n_ Reading conversion rate ----------- --------- ------- ----------------------- square 100% 4,763 16.38% disc 100% 4,759 16.18% disc 110% 4,716 16.09% circle 115% 4,933 15.95% circle 100% 4,872 15.85% circle 110% 4,920 15.53% circle 120% 5,114 15.51% square 115% 4,815 15.51% square 110% 4,927 15.47% circle 105% 5,101 15.33% square 105% 4,775 14.85% disc 115% 4,797 14.78% disc 105% 5,006 14.72% disc 120% 4,912 14.56% square 120% 4,786 13.96% 73,196 15.38% ### Analysis Incorporating visitor type: ~~~{.R} rates <- read.csv(stdin(),header=TRUE) Ul,Size,Type,N,Rate c,120,old,2673,0.1650 c,115,old,2643,0.1854 c,105,new,2636,0.1392 d,105,old,2635,0.1613 s,110,old,2596,0.1749 s,120,old,2593,0.1678 s,105,new,2582,0.1243 d,120,old,2559,0.1649 c,110,new,2558,0.1298 d,110,new,2555,0.1307 c,100,old,2553,0.2002 c,105,old,2539,0.1713 d,115,old,2524,0.1565 s,115,new,2516,0.1391 c,110,old,2505,0.1741 d,100,new,2502,0.1431 c,120,new,2500,0.1284 s,110,new,2491,0.1265 c,115,new,2483,0.1228 d,120,new,2452,0.1277 d,105,new,2448,0.1364 c,100,new,2436,0.1199 d,115,new,2435,0.1437 s,100,new,2411,0.1497 s,120,new,2411,0.1161 s,105,old,2387,0.1571 s,115,old,2365,0.1674 d,100,old,2358,0.1735 s,100,old,2329,0.1803 d,110,old,2235,0.1888 rates$Successes <- rates$N * rates$Rate rates$Successes <- round(rates$Successes,0) rates$Failures <-rates$N - rates$Successes g <- glm(cbind(Successes,Failures) ~ Ul * Size + Type, data=rates, family="binomial"); summary(g) # ...Coefficients: # Estimate Std. Error z value Pr(>|z|) # (Intercept) -1.389310 0.270903 -5.13 2.9e-07 # Uld -0.103201 0.386550 -0.27 0.789 # Uls 0.055036 0.389109 0.14 0.888 # Size -0.004397 0.002458 -1.79 0.074 # Uld:Size 0.000842 0.003509 0.24 0.810 # Uls:Size -0.000741 0.003533 -0.21 0.834 # Typeold 0.317126 0.020507 15.46 < 2e-16 summary(step(g)) # ...Coefficients: # Estimate Std. Error z value Pr(>|z|) # (Intercept) -1.40555 0.15921 -8.83 <2e-16 # Size -0.00436 0.00144 -3.02 0.0025 # Typeold 0.31725 0.02051 15.47 <2e-16 ## examine just the list type alone, since the Size result is clear. summary(glm(cbind(Successes,Failures) ~ Ul + Type, data=rates, family="binomial")) # ...Coefficients: # Estimate Std. Error z value Pr(>|z|) # (Intercept) -1.8725 0.0208 -89.91 <2e-16 # Uld -0.0106 0.0248 -0.43 0.67 # Uls -0.0265 0.0249 -1.07 0.29 # Typeold 0.3163 0.0205 15.43 <2e-16 summary(glm(cbind(Successes,Failures) ~ Ul + Type, data=rates[rates$Size==100,], family="binomial")) # ...Coefficients: # Estimate Std. Error z value Pr(>|z|) # (Intercept) -1.8425 0.0465 -39.61 < 2e-16 # Uld -0.0141 0.0552 -0.26 0.80 # Uls 0.0353 0.0551 0.64 0.52 # Typeold 0.3534 0.0454 7.78 7.3e-15 ~~~ The results are a little confusing in factorial form: it seems pretty clear that `Size` is bad and that 100% performs best, but what's going on with the list icon type? Do we have too little data or is it interacting with the font size somehow? I find it a lot clearer when plotted: ~~~{.R} library(ggplot2) qplot(Size,Rate,color=Ul,data=rates) ~~~ ![Reading rate, split by font size, then by list icon type](/images/ab/2013-10-27-ulfontsize.png) Immediately the negative effect of increasing the font size jumps out, but it's easier to understand the list icon estimates: square performs the best in the 100% (the original default) font size condition but it performs poorly in the other font sizes, which is why it seems to do only medium-well compared to the others. Given how much better 100% performs than the others, I'm inclined to ignore their results and keep the squares. 100% and squares, however, were the original CSS settings, so this means I will make no changes to the existing CSS based on these results. ## Blockquote formatting Another bit of formatting I've been meaning to test for a while is seeing how well [Readability](http://www.readability.com/)'s pull-quotes next to blockquotes perform, and to check whether my zebra-striping of nested blockquotes is helpful or harmful. The Readability thing goes like this: ~~~{.Css} blockquote: : before { content: "\201C"; filter: alpha(opacity=20); font-family: "Constantia", Georgia, 'Hoefler Text', 'Times New Roman', serif; font-size: 4em; left: -0.5em; opacity: .2; position: absolute; top: .25em } ~~~ The current blockquote striping goes thusly: ~~~{.Css} blockquote, blockquote blockquote blockquote, blockquote blockquote blockquote blockquote blockquote { z-index: -2; background-color: rgb(245, 245, 245); } blockquote blockquote, blockquote blockquote blockquote blockquote, blockquote blockquote blockquote blockquote blockquote blockquote { background-color: rgb(235, 235, 235); } ~~~ ### Implementation This is another 2x2 design since we can use the Readability quotes or not, and the zebra-striping or not. ~~~{.Diff} hunk ./static/css/default.css 271 -blockquote, blockquote blockquote blockquote, - blockquote blockquote blockquote blockquote blockquote { - z-index: -2; - background-color: rgb(245, 245, 245); } -blockquote blockquote, blockquote blockquote blockquote blockquote, - blockquote blockquote blockquote blockquote blockquote blockquote { - background-color: rgb(235, 235, 235); } +/* blockquote, blockquote blockquote blockquote, */ +/* blockquote blockquote blockquote blockquote blockquote { */ +/* z-index: -2; */ +/* background-color: rgb(245, 245, 245); } */ +/* blockquote blockquote, blockquote blockquote blockquote blockquote, */ +/*blockquote blockquote blockquote blockquote blockquote blockquote { */ +/* background-color: rgb(235, 235, 235); } */ hunk ./static/templates/default.html 30 -
+
hunk ./static/templates/default.html 148 - ulFontSize: [ + blockquoteFormatting: [ hunk ./static/templates/default.html 150 - name: 's100', - "ulFontSize_class1": "", - "ulFontSize_class2": "" + name: 'rz', + "blockquoteFormatting_class1": "", + "blockquoteFormatting_class2": "" hunk ./static/templates/default.html 155 - name: 's105', - "ulFontSize_class1": "", - "ulFontSize_class2": "" + name: 'orz', + "blockquoteFormatting_class1": "", + "blockquoteFormatting_class2": "" hunk ./static/templates/default.html 160 - name: 's110', - "ulFontSize_class1": "", - "ulFontSize_class2": "" + name: 'roz', + "blockquoteFormatting_class1": "", + "blockquoteFormatting_class2": "" hunk ./static/templates/default.html 165 - name: 's115', - "ulFontSize_class1": "", - "ulFontSize_class2": "" - }, - { - name: 's120', - "ulFontSize_class1": "", - "ulFontSize_class2": "" - }, - { - name: 'c100', - "ulFontSize_class1": "", - "ulFontSize_class2": "" - }, - { - name: 'c105', - "ulFontSize_class1": "", - "ulFontSize_class2": "" - }, - { - name: 'c110', - "ulFontSize_class1": "", - "ulFontSize_class2": "" - }, - { - name: 'c115', - "ulFontSize_class1": "", - "ulFontSize_class2": "" - }, - { - name: 'c120', - "ulFontSize_class1": "", - "ulFontSize_class2": "" - }, - { - name: 'd100', - "ulFontSize_class1": "", - "ulFontSize_class2": "" - }, - { - name: 'd105', - "ulFontSize_class1": "", - "ulFontSize_class2": "" - }, - { - name: 'd110', - "ulFontSize_class1": "", - "ulFontSize_class2": "" - }, - { - name: 'd115', - "ulFontSize_class1": "", - "ulFontSize_class2": "" - }, - { - name: 'd120', - "ulFontSize_class1": "", - "ulFontSize_class2": "" + name: 'oroz', + "blockquoteFormatting_class1": "", + "blockquoteFormatting_class2": "" ... ]] ~~~ ### Data Readability Quote Blockquote highlighting N Conversion Rate -------------------- ----------------------- ------ --------------- no yes 11,663 20.04% yes yes 11,514 19.86% no no 11,464 19.21% yes no 10,669 18.51% 45,310 19.42% I discovered during this experiment that I could graph the conversion rate of each condition separately: ![Google Analytics view on blockquote factorial test conversions, by day](/images/ab/2013-11-25-blockquotehighlighting.png) What I like about this graph is how it demonstrates some basic statistical points: 1. the more traffic, the smaller sampling error is and the closer the 4 conditions are to their true values as they cluster together. This illustrates how even what *seems* like a large difference based on a large amount of data, may still be - unintuitively - dominated by sampling error 2. day to day, any condition can be on top; no matter which one proves superior and which version is the worst, we can spot days where the worst version looks better than the best version. This illustrates how insidious selection biases or choice of datapoints can be: we can easily lie and show black is white, if we can just manage to cherrypick a little bit. 3. the underlying traffic does not itself appear to be completely stable or consistent. There are a lot of movements which look like the underlying visitors may be changing in composition slightly and responding slightly. This harks back to the paper's warning that for some tests, no answer was possible as the responses of visitors kept changing which version was performing best. ### Analysis ~~~{.R} rates <- read.csv(stdin(),header=TRUE) Readability,Zebra,Type,N,Rate FALSE,FALSE,new,7191,0.1837 TRUE,TRUE,new,7182,0.1910 FALSE,TRUE,new,7112,0.1800 TRUE,FALSE,new,6508,0.1804 FALSE,TRUE,old,4652,0.2236 TRUE,FALSE,old,4452,0.1995 TRUE,TRUE,old,4412,0.2201 FALSE,FALSE,old,4374,0.2046 rates$Successes <- rates$N * rates$Rate rates$Successes <- round(rates$Successes,0) rates$Failures <-rates$N - rates$Successes g <- glm(cbind(Successes,Failures) ~ Readability * Zebra + Type, data=rates, family="binomial"); summary(g) # ...Coefficients: # Estimate Std. Error z value Pr(>|z|) # (Intercept) -1.5095 0.0255 -59.09 <2e-16 # ReadabilityTRUE -0.0277 0.0340 -0.81 0.42 # ZebraTRUE 0.0327 0.0331 0.99 0.32 # ReadabilityTRUE:ZebraTRUE 0.0609 0.0472 1.29 0.20 # Typeold 0.1788 0.0239 7.47 8e-14 summary(step(g)) # ...Coefficients: # Estimate Std. Error z value Pr(>|z|) # (Intercept) -1.5227 0.0197 -77.20 < 2e-16 # ZebraTRUE 0.0627 0.0236 2.66 0.0079 # Typeold 0.1782 0.0239 7.45 9.7e-14 ~~~ The top-performing variant is the status quo (no Readability-style quote, zebra-striped blocks). So we keep it. ## Font size & ToC background It was pointed out to me that in my previous font-size test, the clear linear trend may have implied that larger fonts than 100% were bad, but that I was making an unjustified leap in implicitly assuming that 100% was best: if bigger is worse, then mightn't the optimal font size be something *smaller* than 100%, like 95%? And while the blockquote background coloring is a good idea, per the previous test, what about the other place on `gwern.net` where I use a light background shading: the Table of Contents? Perhaps it would be better with the same background shading as the blockquotes, or no shading? Finally, because I am tired of just 2 factors, I throw in a third factor to make it really multifactorial. I picked the number-sizing from the existing list of suggestions. Each factor has 3 variants, giving 27 conditions: ~~~{.Css} .num { font-size: 85%; } .num { font-size: 95%; } .num { font-size: 100%; } html { font-size: 85%; } html { font-size: 95%; } html { font-size: 100%; } div#TOC { background: #fff; } div#TOC { background: #eee; } div#TOC { background-color: rgb(245, 245, 245); } ~~~ ### Implementation ~~~{.Diff} hunk ./static/templates/default.html 30 -
+
hunk ./static/templates/default.html 150 - blockquoteFormatting: [ + tocFormatting: [ hunk ./static/templates/default.html 152 - name: 'rz', - "blockquoteFormatting_class1": "", - "blockquoteFormatting_class2": "" + name: '88f', + "tocFormatting_class1": "", + "tocFormatting_class2": "" hunk ./static/templates/default.html 157 - name: 'orz', - "blockquoteFormatting_class1": "", - "blockquoteFormatting_class2": "" + name: '88e', + "tocFormatting_class1": "", + "tocFormatting_class2": "" hunk ./static/templates/default.html 162 - name: 'oroz', - "blockquoteFormatting_class1": "", - "blockquoteFormatting_class2": "" + name: '88r', + "tocFormatting_class1": "", + "tocFormatting_class2": "" + }, + { + name: '89f', + "tocFormatting_class1": "", + "tocFormatting_class2": "" + }, + { + name: '89e', + "tocFormatting_class1": "", + "tocFormatting_class2": "" + }, + { + name: '89f', + "tocFormatting_class1": "", + "tocFormatting_class2": "" + }, + { + name: '81f', + "tocFormatting_class1": "", + "tocFormatting_class2": "" + }, + { + name: '81e', + "tocFormatting_class1": "", + "tocFormatting_class2": "" + }, + { + name: '81r', + "tocFormatting_class1": "", + "tocFormatting_class2": "" + }, + { + name: '98f', + "tocFormatting_class1": "", + "tocFormatting_class2": "" + }, + { + name: '98e', + "tocFormatting_class1": "", + "tocFormatting_class2": "" + }, + { + name: '98r', + "tocFormatting_class1": "", + "tocFormatting_class2": "" + }, + { + name: '99f', + "tocFormatting_class1": "", + "tocFormatting_class2": "" + }, + { + name: '99e', + "tocFormatting_class1": "", + "tocFormatting_class2": "" + }, + { + name: '99f', + "tocFormatting_class1": "", + "tocFormatting_class2": "" + }, + { + name: '91f', + "tocFormatting_class1": "", + "tocFormatting_class2": "" + }, + { + name: '91e', + "tocFormatting_class1": "", + "tocFormatting_class2": "" + }, + { + name: '91r', + "tocFormatting_class1": "", + "tocFormatting_class2": "" + }, + { + name: '18f', + "tocFormatting_class1": "", + "tocFormatting_class2": "" + }, + { + name: '18e', + "tocFormatting_class1": "", + "tocFormatting_class2": "" + }, + { + name: '18r', + "tocFormatting_class1": "", + "tocFormatting_class2": "" + }, + { + name: '19f', + "tocFormatting_class1": "", + "tocFormatting_class2": "" + }, + { + name: '19e', + "tocFormatting_class1": "", + "tocFormatting_class2": "" + }, + { + name: '19f', + "tocFormatting_class1": "", + "tocFormatting_class2": "" + }, + { + name: '11f', + "tocFormatting_class1": "", + "tocFormatting_class2": "" + }, + { + name: '11e', + "tocFormatting_class1": "", + "tocFormatting_class2": "" + }, + { + name: '11r', + "tocFormatting_class1": "", + "tocFormatting_class2": "" ... ]] ~~~ ### Analysis ~~~{.R} rates <- read.csv(stdin(),header=TRUE) NumSize,FontSize,TocBg,Type,N,Rate 1,9,e,new,3060,0.1513 8,9,e,new,2978,0.1605 9,1,r,new,2965,0.1548 8,8,f,new,2941,0.1629 1,9,f,new,2933,0.1558 9,9,r,new,2932,0.1576 8,9,f,new,2906,0.1473 1,9,r,new,2901,0.1482 9,9,f,new,2901,0.1420 8,8,r,new,2885,0.1567 1,8,e,new,2876,0.1412 8,1,r,new,2869,0.1593 9,8,f,new,2846,0.1472 1,1,e,new,2844,0.1551 1,8,f,new,2841,0.1457 9,8,e,new,2834,0.1478 8,1,f,new,2833,0.1521 1,8,r,new,2818,0.1544 8,8,e,new,2818,0.1678 8,1,e,new,2810,0.1605 1,1,r,new,2806,0.1775 9,8,r,new,2801,0.1682 9,1,e,new,2799,0.1422 8,9,r,new,2764,0.1548 9,9,e,new,2753,0.1478 1,1,f,new,2750,0.1611 9,1,f,new,2700,0.1537 8,8,r,old,1551,0.2521 9,8,e,old,1519,0.2146 9,8,f,old,1505,0.2153 1,8,e,old,1489,0.2317 1,1,e,old,1475,0.2339 8,1,f,old,1416,0.2112 1,9,r,old,1390,0.2245 8,9,e,old,1388,0.2464 9,9,r,old,1379,0.2466 8,9,r,old,1374,0.1907 1,9,f,old,1361,0.2337 8,8,f,old,1348,0.2322 1,9,e,old,1347,0.2279 1,8,f,old,1340,0.2470 9,1,r,old,1336,0.2605 8,1,r,old,1326,0.2119 8,8,e,old,1321,0.2286 9,1,f,old,1318,0.2398 1,1,r,old,1293,0.2111 1,8,r,old,1293,0.2073 9,9,f,old,1261,0.2411 8,9,f,old,1254,0.2113 9,9,e,old,1240,0.2435 1,1,f,old,1232,0.2240 8,1,e,old,1229,0.2587 9,1,e,old,1182,0.2335 9,8,r,old,1032,0.2403 rates[rates$NumSize==1,]$NumSize <- 100 rates[rates$NumSize==9,]$NumSize <- 95 rates[rates$NumSize==8,]$NumSize <- 85 rates[rates$FontSize==1,]$FontSize <- 100 rates[rates$FontSize==9,]$FontSize <- 95 rates[rates$FontSize==8,]$FontSize <- 85 rates$Successes <- rates$N * rates$Rate rates$Successes <- round(rates$Successes,0) rates$Failures <-rates$N - rates$Successes g <- glm(cbind(Successes,Failures) ~ NumSize * FontSize * TocBg + Type, data=rates, family="binomial"); summary(g) # ...Coefficients: # Estimate Std. Error z value Pr(>|z|) # (Intercept) 0.124770 3.020334 0.04 0.97 # NumSize -0.022262 0.032293 -0.69 0.49 # FontSize -0.012775 0.032283 -0.40 0.69 # TocBgf 4.042812 4.287006 0.94 0.35 # TocBgr 5.356794 4.250778 1.26 0.21 # NumSize:FontSize 0.000166 0.000345 0.48 0.63 # NumSize:TocBgf -0.040645 0.045855 -0.89 0.38 # NumSize:TocBgr -0.054164 0.045501 -1.19 0.23 # FontSize:TocBgf -0.052406 0.045854 -1.14 0.25 # FontSize:TocBgr -0.065503 0.045482 -1.44 0.15 # NumSize:FontSize:TocBgf 0.000531 0.000490 1.08 0.28 # NumSize:FontSize:TocBgr 0.000669 0.000487 1.37 0.17 # Typeold 0.492688 0.015978 30.84 <2e-16 summary(step(g)) # ...Coefficients: # Estimate Std. Error z value Pr(>|z|) # (Intercept) 3.808438 1.750144 2.18 0.0295 # NumSize -0.059730 0.018731 -3.19 0.0014 # FontSize -0.052262 0.018640 -2.80 0.0051 # TocBgf -0.844664 0.285387 -2.96 0.0031 # TocBgr -0.747451 0.283304 -2.64 0.0083 # NumSize:FontSize 0.000568 0.000199 2.85 0.0044 # NumSize:TocBgf 0.008853 0.003052 2.90 0.0037 # NumSize:TocBgr 0.008139 0.003030 2.69 0.0072 # Typeold 0.492598 0.015975 30.83 <2e-16 ~~~ The two size tweaks turn out to be unambiguously negative compared to the status quo (with an almost negligible interaction term probably reflecting reader preference for consistency in sizes of letters and numbers - as one gets smaller, the other does better if it's smaller too). The Table of Contents backgrounds also survive (thanks to the new vs old visitor type covariate adding power): there were 3 background types, `e`/`f`/`r`[gb], and `f`/`r` turn out to have negative coefficients, implying that `e` is best - but `e` is also the status quo, so no change is recommended. ### Multifactorial roundup At this point it seems worth asking whether running multifactorials has been worthwhile. The analysis is a bit more difficult, and the more factors there are, the harder to interpret. I'm also not too keen on encoding the combinatorial explosion into a big JS array for ABalytics. In my tests so far, have there been many interactions? A quick tally of the `glm()`/`step()` results: 1. Text & background color: - original: 2 main, 1 two-way interaction - survived: 2 main, 1 two-way interaction 2. List symbol and font-size: - original: 3 main, 2 two-way interactions - survived: 1 main 3. Blockquote formatting: - original: 2 main, 1 two-way - survived: 1 main 4. Font size & ToC background: - original: 4 mains, 5 two-ways, 2 three-ways - survived: 3 mains, 2 two-way So of the 11 main effects, 9 two-ways, & 2 three-ways, there were confirmed in the reduced models: 7 mains, 3 two-ways (22%), & 0 three-ways (0%). And of the 2 interactions, only the black/white interaction was important (and even there, if I had regressed instead `cbind(Successes, Failures) ~ Black + White`, black & white would still have positive coefficients, they just would not be statistically-significant, and so I would likely have made the same choice as I did with the interaction data available). This is not a resounding endorsement so far. ## Section header capitalization 3x3: - `h1, h2, h3, h4, h5 { text-transform: uppercase; }` - `h1, h2, h3, h4, h5 { text-transform: none; }` - `h1, h2, h3, h4, h5 { text-transform: capitalize; }` - `div#header h1 { text-transform: uppercase; }` - `div#header h1 { text-transform: none; }` - `div#header h1 { text-transform: capitalize; }` ~~~{.Diff} --- a/static/templates/default.html +++ b/static/templates/default.html @@ -27,7 +27,7 @@ -
+