What Significance is
Significance in statistics is a measure of how likely it is that an observed difference between two or more groups is due to a real difference between the groups and not due to chance. It is calculated by performing a statistical test which compares the observed data to a null hypothesis.
The process of determining statistical significance involves the following steps:
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State the null hypothesis: This is the hypothesis which states that there is no difference between the two groups being compared.
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Select the appropriate test: Depending on the type of data, different tests will be more appropriate.
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Calculate the test statistic: This is the numerical value that is used to evaluate the hypothesis.
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Determine the p-value: This is the probability of obtaining the observed data, given that the null hypothesis is true.
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Compare the p-value to the significance level: The significance level is typically set at 0.05 or 0.01. If the p-value is less than the significance level, then the null hypothesis can be rejected and the difference is considered statistically significant.
Examples
- An experimenter may use statistical significance tests to assess the validity of their research findings.
- A researcher may use statistical significance tests to determine if two variables are significantly associated.
- A data analyst may use statistical significance tests to determine if there is a significant difference between two groups or samples.
- A statistician may use statistical significance tests to determine if a correlation or regression equation is statistically significant.
- A business analyst may use statistical significance tests to determine if a trend in a dataset is real or just random variation.