What Is Statistical Significance?
Let me explain statistical significance directly: it's my determination as an analyst that the results you're seeing in the data aren't just happening by chance. I make this call using statistical hypothesis testing, which gives you a p-value—that's the probability of getting results as extreme as these if everything was purely random.
You'll often hear that a p-value of 5% or lower means it's statistically significant. That's the threshold I and many others use.
Key Takeaways
- Statistical significance means a relationship between variables is caused by something beyond chance.
- It provides evidence against the null hypothesis, which assumes only random chance is at play in the data.
- We use statistical hypothesis testing to check if a data set's results are significant.
- A p-value of 5% or lower is typically seen as statistically significant.
Understanding Statistical Significance
Think of statistical significance as testing the null hypothesis, which says your results are just due to chance. If the p-value is small enough, your data set shows significance.
When the p-value is large, the results fit with chance alone, so they align with the null hypothesis—though it doesn't prove it outright. But if it's small, say 5% or less, chance doesn't explain it well, and you reject the null in favor of a real explanation.
This is important in areas like new drug trials, vaccine tests, and pathology studies for checking effectiveness. As an investor, you can use it to gauge how well a company is doing with new products.
Examples of Statistical Significance
Suppose you're like Alex, a financial analyst wondering if some investors knew about a company's failure ahead of time. You compare average daily market returns before and after the failure to see if there's a significant difference.
If the p-value comes out at 28%—that's over 5%—it means the difference (-0.0033 to +0.0007) isn't unusual under chance alone, so no strong evidence of advance knowledge.
But if it's 0.01%, way under 5%, that difference is rare by chance, so you might reject the null and dig deeper into possible insider trading.
We also see this in testing new medical products like drugs, devices, and vaccines. Reports on significance tell investors about a company's product success. For instance, if a diabetes drug company reports a significant reduction in type 1 diabetes after 26 weeks with a 4% p-value, it signals real effectiveness to investors and regulators. Announcements like this often move pharmaceutical stock prices.
How Is Statistical Significance Determined?
You determine it through statistical hypothesis testing, checking if data can be explained by chance alone. It's all about the null hypothesis assuming chance is the only factor. Rejecting that null means the data is statistically significant.
What Is P-Value?
The p-value measures the probability that your observed difference happened by random chance. If it's small—5% or less—the results aren't easily chalked up to chance, so you reject the null. If it's over 5%, chance explains it, and the data fits the null hypothesis.
How Is Statistical Significance Used?
It's commonly used to test new medical products like drugs, devices, and vaccines for effectiveness. Public reports on significance help investors see how successful a company is with new releases, and these can strongly affect pharmaceutical stock prices.
The Bottom Line
In the end, statistical significance comes from hypothesis testing yielding a p-value that shows if variables are linked beyond chance. The 5% mark is usually the cutoff—the lower it is, the more significant. This testing is key for drug trials, and if you're an investor, it helps evaluate companies launching new products.
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