Hypothesis Testing Explanation Tool

The Null Hypothesis ($H_0$)

This is the default assumption, or the status quo. It usually states that there is "no effect" or "no difference." We conduct our test to see if we have enough evidence to challenge this assumption.

Define a Null Hypothesis:

The Alternative Hypothesis ($H_1$ or $H_a$)

This is what we are trying to prove. It's the claim or theory that contradicts the null hypothesis. It states that there *is* an effect or a difference.

Define an Alternative Hypothesis:

Significance Level ($\alpha$)

Before we collect data, we must set a "significance level," denoted by the Greek letter alpha ($\alpha$). This is the threshold for how surprised we need to be by our data to reject the null hypothesis.

It represents the probability of rejecting the null hypothesis when it is actually true (a "Type I Error"). A lower alpha means we require stronger evidence.

Choose a Significance Level: 0.05 (Most common) 0.01 (Requires strong evidence) 0.10 (Requires weaker evidence)

The p-value

After conducting an experiment and analyzing the data, we calculate a "p-value."

The p-value is the probability of observing our data (or more extreme data) *if the null hypothesis were true*.

• A small p-value suggests that our observed data is very unlikely under the null hypothesis. This is evidence *against* the null hypothesis.
• A large p-value suggests that our data is quite plausible under the null hypothesis. This is *not* strong evidence against the null hypothesis.

For this example, enter a p-value you might have found: