p-value Facts for Kids

In statistics, a p-value helps scientists and researchers understand if their experiment results are meaningful. It's a number that tells you how likely it is to get a certain result if your main idea (called the null hypothesis) is actually wrong. Think of the null hypothesis as the "nothing special is happening" idea.

If the p-value is small, it means your experiment results are probably not just due to chance. This suggests that the "nothing special" idea is likely incorrect. Instead, your experiment might be showing something important. This is called having statistical significance.

For many science projects, a p-value needs to be less than 0.05. This means there's less than a 5% chance that your results happened randomly. If your p-value is this low, it gives strong support for a different idea, called the alternative hypothesis. So, a low p-value means your experiment likely found something real and important.

What is a p-value?
Why is 0.05 important?
- What 0.05 Means
- It's Not a Guarantee
Who developed the p-value?
- Early Ideas
- Modern Use
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What is a p-value?

A p-value is a number between 0 and 1. It helps us decide if an experiment's results are strong enough to support a new idea. Imagine you are testing a new fertilizer on plants.

The "Null Hypothesis" Explained

Before you start, you have a main idea called the null hypothesis. This idea usually says there is no difference or no effect. For example, your null hypothesis might be: "This new fertilizer has no effect on plant growth."

Testing Your Idea

You then do your experiment. You give some plants the new fertilizer and others just water. After a few weeks, you measure how much they grew.

How p-value Helps

The p-value tells you: If the fertilizer truly had no effect (if the null hypothesis was true), how likely would you be to see the growth difference you measured just by chance?

If the p-value is high (like 0.60 or 60%), it means your results could easily happen by chance. So, the fertilizer probably didn't make a difference.
If the p-value is low (like 0.01 or 1%), it means your results are very unlikely to happen by chance. This suggests the fertilizer probably did make a difference!

Why is 0.05 important?

In many areas of science, a p-value of less than 0.05 is considered a "significant" result. This means there's less than a 5% chance that your findings are just random.

What 0.05 Means

When a p-value is below 0.05, scientists often say they can "reject the null hypothesis." This means they have good evidence that their new idea (the alternative hypothesis) is likely true. For example, if your fertilizer experiment had a p-value of 0.03, you could say the fertilizer likely helps plants grow.

It's Not a Guarantee

Remember, a low p-value doesn't mean your new idea is 100% true. It just means it's very unlikely that your results happened by accident. Science often needs many experiments to be sure about a new discovery.

Who developed the p-value?

The idea of using probability to test hypotheses has a long history.

Early Ideas

John Arbuthnot (1710): An English doctor and mathematician, he used probability to show that more boys being born than girls was probably not just random chance.
Pierre-Simon Laplace (1770s): A French mathematician, he used similar ideas to study the birth rates of boys and girls in Paris.

Modern Use

Karl Pearson (early 1900s): A British mathematician, he developed the "chi-squared test," which uses p-values to compare observed results with expected results.
Ronald Fisher (1920s): A British statistician, he made the p-value a central part of how scientists test their ideas. He suggested using the 0.05 level as a common standard.

Today, the p-value is a key tool in many fields, from medicine to psychology, helping researchers make sense of their data.