Last time you learned about uncertainty in your data. But what happens when you need to make a specific business decision, like "Should we launch this new product?"

You need a systematic way to evaluate claims using data. This is where hypothesis testing becomes your decision-making framework.

Engagement Message

Ready to test business claims scientifically?

Hypothesis testing gives you a structured approach to answer yes/no business questions. Instead of guessing, you use data to evaluate competing explanations.

Think of it like a courtroom trial - you start with an assumption and then examine evidence to see if it supports or contradicts that assumption.

Engagement Message

What business decisions have you made recently that could benefit from this approach?

Every hypothesis test starts with two competing statements. The null hypothesis assumes "no effect" or "no difference" - essentially the status quo.

For example: "Our new website design doesn't improve conversion rates" (null hypothesis).

The alternative hypothesis claims there IS an effect: "Our new design DOES improve conversions."

Engagement Message

Which hypothesis would you prefer to be true?

Here's the key insight: we always start by assuming the null hypothesis is true. Why? Because we need strong evidence to justify making changes or investments.

It's like presuming innocence in court - you need convincing evidence to prove guilt.

This protects you from making costly changes based on random fluctuations.

Engagement Message

Does this "innocent until proven guilty" approach make sense for business decisions?

The p-value tells you: "If nothing really changed, what's the probability of seeing results this extreme or more extreme?"

A low p-value (typically below 0.05) suggests your results are unlikely to be just random chance.