So far in this module, we have been testing numbers. T-Tests and ANOVA are designed for continuous, quantitative data—things you can measure, like test scores, weight loss, or revenue. But what happens when your data isn’t a number at all?…
If you have 4 different landing pages (A, B, C, and D), your first instinct might be to just run a bunch of separate T-Tests. Never do this. Running multiple T-tests destroys your Alpha level. Remember that an Alpha of…
In the first lesson of this module, we established the rules of proof: set a Null Hypothesis, demand overwhelming evidence (Alpha), and calculate a P-Value. But how do you actually get that P-Value? If you are trying to compare two…
Imagine you want to know the average age of a customer buying your software. You obviously cannot track down every single user in the world to ask them. Instead, you pull a random sample of 500 customers and find their…
Imagine you are the Lead Data Scientist for a major e-commerce company. The design team just launched a new, bright orange checkout button. After one week, the data rolls in: sales with the orange button are 2% higher than the…
Open almost any published scientific paper, scroll to the results section, and you will find it: $p < .05$. It is the holy grail of research. It is the threshold that determines if a drug gets approved, if a paper…
If you run a t-test and get a statistically significant p-value (like $p < 0.05$), you know that a difference exists between your two groups. But there is a catch: a p-value does not tell you how large or important…