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A/B Testing

Algorithms

Analytics

Machine Learning

Probability

Product Metrics

Python

Statistics

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Say we have a list of <code>existing_ids</code> that we have already scraped. Let’s say we also have two lists, one of <code>names</code> and another of <code>urls</code> that correspond to <code>names</code> with the id of the names in the <code>url</code>.

Write a function <code>new_resumes</code> to return the names and ids for ids that we haven’t scraped yet.

Example:

Input:

<pre tabindex="0" class="chroma"><code>existing_ids = [15234, 20485, 34536, 95342, 94857]

names = [&#39;Calvin&#39;, &#39;Jason&#39;, &#39;Cindy&#39;, &#39;Kevin&#39;]
urls = [
 &#39;domain.com/resume/15234&#39;, 
 &#39;domain.com/resume/23645&#39;, 
 &#39;domain.com/resume/64337&#39;, 
 &#39;domain.com/resume/34536&#39;,
]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def new_resumes(existing_ids,names,urls) -&gt;
output = [(&#39;Jason&#39;, 23645), (&#39;Cindy&#39;, 64337)]
</code></pre>

Write a function to return the names and ids for ids that we haven't scraped yet.

New Resumes

Let’s say we have a table with an <code>id</code> and <code>name</code> fields. The table holds over 100 million rows and we want to sample a random row in the table without throttling the database.

Write a query to randomly sample a row from this table.

Input:

<code>big_table</code> table

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

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

<tr>
<td><code>name</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>

Write a query to randomly sample a row from a big table.

Random SQL Sample

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

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

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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