In the world of statistics and data science, the term “p-value” often comes up, but many people find it confusing. The definition is simple; however, understanding it can be a bit confusing.
This article is prepared to make it easy to understand. You don’t need to be a data scientist or a genius university student.
Understanding P-Value
The first thing you should know about the p-value is that it shows whether your result is important when testing an idea (or hypothesis).
It indicates how likely it is to observe your results if there is actually no effect (this assumption is known as the null hypothesis).
The null hypothesis is simply the assumption that there is no effect or difference. So basically, the null hypothesis indicates that there is no effect in what you are testing.
The Basics of Hypothesis Testing
Null Hypothesis (H0): This is the idea that there is no effect or difference. For example, if you are testing a new type of fertilizer, the null hypothesis might say that the new fertilizer does not help plants grow better than no fertilizer at all.
Alternative Hypothesis (H1): This is what you want to prove. Using the previous example, the alternative hypothesis would suggest that the new fertilizer does help plants grow better than no fertilizer at all.
Significance Level (α): Before running a test, researchers choose a significance level, usually set at 0.05. This level helps decide how strong the evidence needs to be to reject the null hypothesis.
Reading P-Value
Now that you understand the simple terms, how do you know what your p-value indicates? It’s simple: if your p-value is higher than 0.05, it means there is not enough evidence to reject the null hypothesis.
However, if it’s lower than 0.05 (or the value you set before starting), it means there is enough evidence to reject the null hypothesis, supporting the idea that the alternative hypothesis may be true.
Low P-Value (≤ 0.05): Results are statistically significant.
High P-Value (> 0.05): Results are not statistically significant.
Additional Information
If you are preparing for an exam, this section can be important for you because it provides additional key information on the subject.
A common mistake is thinking that the p-value shows the chance that the null hypothesis is true. Instead, it tells you how likely it is to see your data if the null hypothesis is true.
For example, if you are testing a new medication and get a p-value of 0.03, it does not mean there is a 3% chance that the null hypothesis (that the medication has no effect) is true. Instead, it means that if the medication really had no effect, there is only a 3% chance of getting the results you observed.
Secondly, a small p-value does not indicate the size or importance of an effect. It simply shows whether the effect is statistically significant (so it says “yes, it’s effective,” but it doesn’t tell you how effective).
Conclusion
The p-value is an essential concept that is actually quite simple to understand. While diving into the theory behind p-values can sometimes be frustrating, it’s often more helpful to focus on practical examples. By doing so, you can better grasp how p-values work and their significance in real-world situations.
