# Statistical Significance

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## Definition of 'Statistical Significance'

Statistical significance is a statistical concept that refers to the probability of an event occurring by chance. In finance, statistical significance is often used to determine whether a particular investment strategy or trading method is effective.

To determine statistical significance, a researcher will typically compare the results of their study to what would be expected to occur by chance. If the results of the study are significantly different from what would be expected by chance, then the researcher can conclude that the study has found a statistically significant result.

There are a number of different ways to calculate statistical significance. One common method is to use a p-value. The p-value is the probability of obtaining the results of the study if the null hypothesis is true. The null hypothesis is the hypothesis that there is no difference between the two groups being studied.

If the p-value is less than a certain threshold, typically 0.05, then the researcher can conclude that the results of the study are statistically significant. This means that there is a low probability that the results of the study occurred by chance.

It is important to note that statistical significance does not necessarily mean that the results of the study are important or meaningful. A study may be statistically significant, but it may not have any practical implications.

For example, a study may find that a particular investment strategy has a statistically significant return. However, the return may be so small that it is not worth the risk of investing in the strategy.

Therefore, it is important to consider both the statistical significance and the practical implications of a study before making any decisions.

In addition to p-values, there are a number of other statistical tests that can be used to determine statistical significance. These tests include the t-test, the chi-square test, and the ANOVA test.

The t-test is used to compare the means of two groups. The chi-square test is used to compare the frequencies of different outcomes in a study. The ANOVA test is used to compare the means of three or more groups.

These statistical tests can be used to determine whether the results of a study are statistically significant. However, it is important to note that statistical significance does not necessarily mean that the results of the study are important or meaningful.

To determine statistical significance, a researcher will typically compare the results of their study to what would be expected to occur by chance. If the results of the study are significantly different from what would be expected by chance, then the researcher can conclude that the study has found a statistically significant result.

There are a number of different ways to calculate statistical significance. One common method is to use a p-value. The p-value is the probability of obtaining the results of the study if the null hypothesis is true. The null hypothesis is the hypothesis that there is no difference between the two groups being studied.

If the p-value is less than a certain threshold, typically 0.05, then the researcher can conclude that the results of the study are statistically significant. This means that there is a low probability that the results of the study occurred by chance.

It is important to note that statistical significance does not necessarily mean that the results of the study are important or meaningful. A study may be statistically significant, but it may not have any practical implications.

For example, a study may find that a particular investment strategy has a statistically significant return. However, the return may be so small that it is not worth the risk of investing in the strategy.

Therefore, it is important to consider both the statistical significance and the practical implications of a study before making any decisions.

In addition to p-values, there are a number of other statistical tests that can be used to determine statistical significance. These tests include the t-test, the chi-square test, and the ANOVA test.

The t-test is used to compare the means of two groups. The chi-square test is used to compare the frequencies of different outcomes in a study. The ANOVA test is used to compare the means of three or more groups.

These statistical tests can be used to determine whether the results of a study are statistically significant. However, it is important to note that statistical significance does not necessarily mean that the results of the study are important or meaningful.

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Copyright © 2004-2023, MyPivots. All rights reserved.