A/B Testing: Data-Driven Decision Making
Hello data science enthusiasts, in this article, I will talk about A/B testing. It is a crucial area that proves whether a Data Scientist’s company is making a profit or a loss from the campaigns conducted. Before explaining A/B testing, I will provide you with some brief and important information.
Sampling
Sampling is extracting a subset from a population, assuming that it represents the characteristics of this population well. Let’s say there is a city with an average of 10,000 people. To find the average age of all of them, we would need to go to all 10,000 people, get their ages, and calculate the average. This would require a lot of manpower, so we select an unbiased subset of 100 people that represent the 10,000 people well. This gives us a chance to make a generalization without going through all 10,000 people.
Confidence Intervals
Confidence Intervals involve finding an interval consisting of two numbers that can cover the estimated values of the population parameter. For example, if we have a website, what is the confidence interval for the average time spent? Let’s assume the average is 180 seconds, and the standard deviation is 40 seconds, then the average time spent by users on the website with a 95% confidence interval would be between 172 and 188 seconds.
