In a previous post , I mentioned about expected value and variance of different distributions.

Taking the same statistical concepts further, we now want to compute confidence intervals for our estimate.

**Note:**

- While thinking about Confidence Intervals, it is a good exercise to identify what distribution is representative of your estimate.
- The reason this is needed is because the confidence interval is dependent on standard deviation. As such, it would be necessary to know how you are computing your standard deviation.
- An alternative would be if we compute the variance from base principles.

(https://en.wikipedia.org/wiki/Standard_deviation)

- Here are a couple of very interesting post that explains the relationship between confidence intervals, statistical levels and P-values in a very simple way.
- http://blog.minitab.com/blog/adventures-in-statistics/understanding-hypothesis-tests:-significance-levels-alpha-and-p-values-in-statistics
- https://www.quora.com/Whats-the-difference-between-significance-level-and-confidence-level
- http://sphweb.bumc.bu.edu/otlt/MPH-Modules/EP/EP713_RandomError/EP713_RandomError6.html

- CTR: Here are some interesting discussions around computing confidence intervals for a metric like CTR

**References:**

- http://www.stat.yale.edu/Courses/1997-98/101/confint.htm
- http://onlinestatbook.com/2/estimation/mean.html
- http://www.measuringu.com/blog/ci-10things.php
- https://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval

**Videos:**

- https://www.youtube.com/watch?v=tFWsuO9f74o
- 2 videos here are helpful in understanding the basic concept

**Code:**

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