Statistical significance is a measure of whether an observed result or effect is likely to be real or simply due to chance. In other words, it helps us determine if a difference between two groups or a change over time is meaningful or just a random variation.

From a trading/investing perspective, statistical significance is important because it can help investors make informed decisions about their investments. For example, if a stock’s price suddenly rises or falls, investors may want to know whether this change is significant or just a random fluctuation. If the change is statistically significant, it may indicate that there is a real underlying cause, such as a change in the company’s financial performance or market conditions.

Here’s a simple example: Let’s say you’re interested in investing in two different stocks, A and B. You want to know which one is more likely to give you a higher return on your investment. You look at their historical returns over the past year and find that stock A had an average return of 5% and stock B had an average return of 6%. However, you also notice that the standard deviation of stock A’s returns is much higher than that of stock B.

To determine if the difference in returns between the two stocks is statistically significant, you can perform a statistical test, such as a t-test. The test would tell you if the difference in returns is likely to be real or just due to chance.

If the test shows that the difference in returns is statistically significant, it would suggest that stock B is likely to give you a higher return on your investment than stock A. On the other hand, if the test shows that the difference is not statistically significant, it would suggest that there is no meaningful difference between the two stocks, and you may want to consider other factors when making your investment decision.

What is P-Value? and How it is connected to Statistical Significance?

In statistical hypothesis testing, the p-value is the probability of obtaining a result as extreme or more extreme than the observed result, assuming that the null hypothesis is true. The null hypothesis is the default assumption that there is no significant difference between two groups or no effect of an intervention.

A p-value less than 0.05 (or 5%) is commonly used as the threshold for statistical significance. This means that if the p-value is less than 0.05, there is less than a 5% chance that the observed result was due to chance, assuming the null hypothesis is true. In other words, there is a low probability that the observed result is a false positive.

However, it’s important to note that the choice of the 0.05 threshold is somewhat arbitrary and can vary depending on the field of study and the specific research question. In some cases, a more conservative threshold of 0.01 may be used, while in others a less conservative threshold of 0.10 may be appropriate.

Additionally, statistical significance does not necessarily mean practical or clinical significance. Even if a result is statistically significant, it may not be large enough to be meaningful in a practical sense. Therefore, it’s important to consider the effect size and the context of the research question when interpreting the results.