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<!DOCTYPE html>
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<title>
And yet it moves
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<a href="/" id="title">And yet it moves</a>
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<ul>
<li><a href="/category/data-processing.html">Data processing</a></li>
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<a href="/">Index</a> ¦ <a href="/archives.html">Archives</a>
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<article>
<div class="article_title">
<h3><a href="/challenging-conclusion.html">Challenging the claims and conclusion</a></h3>
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<div class="article_text">
<p>Until now, I have spent time (and <span class="caps">CPU</span> power) to precisely illustrate
what the two methods were designed for:</p>
<ul class="simple">
<li>A/B testing efficiently answers the question <em>is there a difference?</em></li>
<li>The <span class="caps">MAB</span> strategy provides the highest click-through rates over a
wider range of sample sizes.</li>
</ul>
<p>As their respective purposes are ...</p>
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</article>
<hr />
<article>
<div class="article_title">
<h3><a href="/reward-maximisation.html">Reward maximisation</a></h3>
</div>
<div class="article_text">
<div class="section" id="effect-of-the-sample-size">
<h2 id="effect-of-the-sample-size">Effect of the sample size</h2>
<p>I showed in the previous section that the A/B testing data-gathering
method by itself has some advantages over the multi-armed bandit strategy.</p>
<p>But how does this translates to <em>what really matters</em>, clicks?</p>
<p>I first simulated a large number of complete campaigns (i.e. exploration ...</p></div>
</div>
</article>
<hr />
<article>
<div class="article_title">
<h3><a href="/AB-and-MAB.html">A/B testing and the multi-armed bandit</a></h3>
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<div class="article_text">
<div class="section" id="a-b-testing">
<h2 id="ab-testing">A/B testing</h2>
<p>This is exactly why some methods, such as A/B testing
(<a class="reference external" href="https://en.wikipedia.org/wiki/A/B_testing">wiki</a>), were devised:
their only reason of being is to efficiently answer the question
<em>is there actually a difference?</em></p>
<p><em>How?</em> As explained above, A/B testing is a two-staged method…</p>
<p>The <em>exploration stage</em> consists in providing ...</p></div>
</div>
</article>
<hr />
<article>
<div class="article_title">
<h3><a href="/pros-of-AB-testing.html">The pros of the A/B testing method</a></h3>
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<div class="section" id="test-setup">
<h2 id="test-setup">Test setup</h2>
<p>As we have already said, when a (statistical) test is applied to data,
it answers the question for which it was designed with a given
<em>level of significance</em> and <em>power</em>; two components of what we could
improperly call “level of confidence” for simplicity. Here:</p>
<ul class="simple">
<li>The <em>level of significance ...</em></li></ul></div>
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</article>
<hr />
<article>
<div class="article_title">
<h3><a href="/basics.html">Basics of testing for web optimisation</a></h3>
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<div class="article_text">
<p>I recently stumbled across a <a class="reference external" href="https://redd.it/4dlioz">reddit link</a> to
a (now 3-year-old) blog post presenting a multi-armed bandit strategy
claimed to maximize the click-through rate even during the
data-gathering stage of the optimization process.</p>
<p>That original blog post was rather disappointing: many bold claims with
very little or no data, demonstrations ...</p>
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