Imagine you have a coin. Someone tells you it's a fair coin, which means it has an equal chance of landing heads or tails. But you think it might be a trick coin that lands heads more often. How do you decide who's right?

1. You make a guess (hypothesis). Your guess is: "This coin is a trick coin that favors heads."
2. Before you start flipping the coin, you decide what will convince you. Maybe you say, "If I get heads 8 out of 10 times, I'll believe it's a trick coin." This is setting your criterion for decision.
3. You flip the coin 10 times and count the heads.
4. If you get 8 or more heads, you say, "I was right! It's a trick coin." If you get fewer than 8, you say, "I guess it's a fair coin."

In statistics, the process is a bit more complicated, but the idea is the same:

• Null Hypothesis $H_0$: This is the "status quo" or the default statement we want to test. Using the coin example, the null hypothesis would be: "The coin is fair."

• Alternative Hypothesis $H_1$ or $H_a$: This is what you want to prove. In the coin example, it would be: "The coin is not fair."

• Test Statistic: This is like the number of heads you get when you flip the coin. It's a number that helps you decide between the null and alternative hypotheses.

• P-value: This tells you how likely you are to see your test statistic (or something more extreme) if the null hypothesis is true. A small p-value (typically less than 0.05) means the evidence against the null hypothesis is strong.