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A/B Testing

Algorithms

Analytics

Machine Learning

Probability

Product Metrics

Python

Statistics

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<p>Write a query to determine the top 5 actions performed during the month of November 2020, for actions performed on an Apple platform (iPhone, iPad).</p>

<p>The <code>events</code> table tracks every time a user performs a certain action (like post_enter, etc.) on a platform (android, web, etc.).</p>

<p>The output should include the <code>action</code> performed and it’s <code>rank</code> in ascending order. If two actions are performed equally, they should have the same rank. </p>

<p><strong>Example:</strong></p>

<p><strong>Input:</strong></p>

<p><code>events</code> table</p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>user_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>created_at</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>action</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>platform</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>action</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>ranks</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>


Popular Apple Actions

<p>Let’s say that your company is running a standard control and variant AB test on a feature to increase conversion rates on the landing page. The PM checks the results and finds a .04 p-value.</p>

<p>How would you assess the validity of the result?</p>


Experiment Validity

<p>In an A/B test, how can you check if assignment to the various buckets was truly random?</p>


<p>In an A/B test, which of the following is a common method to determine if the assignment to the test groups was truly random?</p>

In an A/B test, which of the following is a common method to determine if the assignment to the test groups was truly random?

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