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Create an <code>Array</code> class simulating the functionality of fixed-size arrays. The array’s size is <code>6</code>. The array’s constructor is already defined:

<pre tabindex="0" class="chroma"><code>class Array:

 def __init__(self):
 pass # Dynamically added to avoid tampering
 self._count = 0
 self._array = [None, None, None, None, None, None]
 self._MAX_CAPACITY = 6
</code></pre>

There is no need to implement the constructor. In the code editor, <code>__init__</code> will contain <code>pass</code>. The constructor’s code will be added dynamically to avoid any changes.

The <code>Array</code> class should have the following methods:

<ul>
<li><code>__len__()</code>: Returns the length of the array. For example, if the array is <code>[1, 2, None, None, None, None, None]</code>, <code>__len__()</code> will return <code>2</code>.</li>

<li><code>__getitem__(index)</code>: Returns the element at the specified index. Raises an <code>IndexError</code> when the index is out of range.</li>

<li><code>emplace_back(element)</code>: Places an element at the back of the array. Raises an <code>ArrayFull</code> exception when the array is full (length &gt; 6).</li>

<li><code>emplace_front(element)</code>: Places an element at the front of the array. Raises an <code>ArrayFull</code> exception when the array is full (length &gt; 6).</li>

<li><code>emplace(element, index)</code>: Places an element at the specified index. Raises an <code>ArrayFull</code> exception when the array is full (length &gt; 6).</li>
</ul>

Note: <code>ArrayFull</code> is already implemented. To raise it, simply use <code>raise ArrayFull()</code>.

Implement the addition operations of fixed length arrays.

Fixed Length Arrays: Addition

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?

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.

How would you assess the validity of the result?

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