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<p>Let’s say that we work for a B2B SaaS company that has been around for three years.</p>

<p>The company has two revenue lines (product_type): the first is labeled as <code>&#34;service&#34;</code> and is a consulting type model where clients are serviced on an hourly rate for a one-off project.
The second revenue line is <code>&#34;software&#34;</code>, which clients can purchase on an ongoing subscription basis.</p>

<p>Given a table of payments data, write a query to calculate the average revenue per client.</p>

<p><em>Note: Round the result to two decimals.</em></p>

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

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

<p><code>payments</code> table</p>

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

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

<tr>
<td><code>user_id</code></td>
<td>NTEGER</td>
</tr>

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

<tr>
<td><code>amount_per_unit</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>quantity</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>product_type</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>average_lifetime_revenue</code></td>
<td>FLOAT</td>
</tr>
</tbody>
</table>


Average Revenue per Customer

<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>You are testing hundreds of hypotheses with many <em>t</em>-tests. What considerations should be made?</p>


<p>What is a key disadvantage of the F-test when compared to multiple t-tests?</p>

What is a key disadvantage of the F-test when compared to multiple t-tests?

Hundreds of Hypotheses

You are testing hundreds of hypotheses with many t-tests. What considerations should be made?

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