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Contents
- Understanding p-value & the Significance Level
- When Your p-value Is Close to 0.05
- Deciphering Statistical Decisions
- Evaluating Real Data
- Striking a Balance
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Understanding p-value & the Significance Level
In the realm of statistics, the p-value and significance level (α) are two critical concepts that lay the groundwork for hypothesis testing. However, understanding their role and how they interact with each other can often be a puzzle, especially when the p-value hovers around 0.05. So, let’s decipher these pieces, one by one.
p-value
The p-value is one of the most misunderstood and widely used concepts in statistics. In layman’s terms, a p-value tells us the probability of obtaining an effect at least as substantial as the one we actually observed in our sample data, assuming that the null hypothesis is true.
Essentially, the p-value is not about the hypothesis being correct; it’s about the data. It indicates how extreme the data is. If our p-value is small, that means the observed data is quite unlikely under the null hypothesis, nudging us towards rejecting it. However, if the p-value is large, we may conclude the data we observed is likely under the null hypothesis, and therefore, we might retain it.
The Significance Level (α)
On the other side of the field stands α, the level of significance. It is the threshold of the probability of incorrectly rejecting a true null hypothesis. Commonly set at 0.05, it means that there is a 5% chance that you will reject the null hypothesis when it is true. α, then, essentially controls the rate of falsely claiming something is significant when it’s not.
