Data science interview questions with answers
The quality and depth of answers here is pretty inconsistent. But this in particular is a pet peeve of mine:<p><i>&gt; Plot a histogram out of the sampled data. If you can fit the bell-shaped &quot;normal&quot; curve to the histogram, then the hypothesis that the underlying random variable follows the normal distribution can not be rejected.</i><p>This is commonly taught in undergrad stats, but you shouldn&#x27;t do this. I&#x27;m of the opinion that normality testing in general is usually a red herring, but this is specifically not a productive way of doing it. Use the other methods. A visual test that relies on how much the histogram approximates a bell curve is very prone to error, because a sample from a variety of other distributions can look visually normal even though it isn&#x27;t.<p>More broadly speaking, the reason I don&#x27;t like this is because it&#x27;s an example of the kind of formulaic, cargo-culted recipes that are often used in statistics without critical thinking. You should strive to obtain a deep understanding of your data and its distribution, and you should be deeply skeptical if the sample you happen to have looks normal. Nature abhors normality, and the central limit theorem can only promise a <i>tendency</i> towards normality as <i>n</i> approaches infinity. It says nothing about what size sample you&#x27;ll practically need for your specific data to be able to treat it as normal.