03-ab.Rmd

```{r, include=FALSE}
knitr::opts_chunk$set(warning = FALSE, message = FALSE) 
```


# A/B Testing Explained

Imagine you're running a website and want to test if a new homepage design increases user engagement compared to the current design. This scenario is perfect for A/B testing, which allows us to make data-driven decisions.

## What is A/B Testing?

A/B testing, also known as split testing, is a method of comparing two versions of a webpage or app against each other to determine which one performs better. Essentially, it's an experiment where two or more variants are shown to users at random, and statistical analysis is used to determine which variation performs better for a given conversion goal.

### Running an A/B Test

Let's set up a simple A/B test example where we compare two versions of a homepage.

### Example Scenario

Suppose you have two versions of a homepage: Version A (the original) and Version B (the new design). You want to know which version keeps users on the site longer.

### Implementing in R

Here's how you can simulate and analyze the results of an A/B test in R:

```{r}
# Simulating time spent on each version of the homepage
set.seed(42)
time_spent_A <- rnorm(100, mean=5, sd=1.5)  # Version A
time_spent_B <- rnorm(100, mean=5.5, sd=1.5)  # Version B

# A/B Testing using t-test
ab_test_result <- t.test(time_spent_A, time_spent_B, alternative = "greater")
ab_test_result
```

### Analyzing Results

The output from the t-test will tell us whether there's a statistically significant difference in the time spent on each version of the homepage. If the p-value is less than 0.05 (assuming a 5% significance level), we can conclude that Version B significantly increases the time users spend on the site.

## Considerations and Best Practices

- **Sample Size:** Ensure you have enough data to detect a meaningful difference if one exists.
- **Segmentation:** Consider running the test on specific user segments to understand different impacts.
- **Duration:** Run the test long enough to account for variability in user behavior but not so long that the market conditions change.