What Does it Mean for the Findings of a Statistical Analysis of Data to be Statistically Significant?

Discover the meaning of statistical significance in data analysis and how it impacts research findings. Learn about p-values, examples, and case studies.


Statistical significance is a crucial concept in research and data analysis. It indicates whether the results of a study are likely to be real and not just due to chance. In this article, we will explore what it means for the findings of a statistical analysis of data to be statistically significant.

Understanding Statistical Significance

Statistical significance is a measure of the probability that a relationship between two or more variables is not due to random chance. In other words, it tells us whether the results observed in a study are likely to be valid or if they could have occurred simply by chance.

When the findings of a statistical analysis are deemed to be statistically significant, it means that the results are unlikely to have occurred by random chance alone. Instead, they suggest that there is a real, underlying relationship between the variables being studied.


Let’s consider an example to illustrate this concept. Suppose a researcher conducts a study to determine if there is a relationship between smoking and the risk of developing lung cancer. After analyzing the data, the researcher finds that smokers are significantly more likely to develop lung cancer than non-smokers.

In this case, if the findings are statistically significant, it means that the observed relationship between smoking and lung cancer is likely to be real and not simply a result of random chance. This provides stronger evidence to support the hypothesis that smoking increases the risk of developing lung cancer.

Interpreting P-Values

One common way to determine statistical significance is through the use of p-values. A p-value is a measure of the strength of the evidence against the null hypothesis. The null hypothesis is the assumption that there is no relationship between the variables being studied.

A p-value of less than 0.05 is typically considered to be statistically significant. This means that there is less than a 5% probability that the results observed are due to chance alone. In contrast, a p-value greater than 0.05 suggests that the results are not statistically significant and could have occurred by random chance.

Case Study

In a clinical trial testing a new drug for a certain condition, the researchers found that patients who took the drug experienced a significant reduction in symptoms compared to those who took a placebo. The p-value for this finding was calculated to be 0.03, indicating that the results are statistically significant.

Based on this information, the researchers can confidently conclude that the new drug is effective in treating the condition, as the likelihood of the results occurring by random chance alone is less than 5%.


Statistical significance is a critical aspect of data analysis that helps researchers determine the validity of their findings. By understanding what it means for the results of a statistical analysis to be statistically significant, researchers can draw more reliable conclusions from their studies and make informed decisions based on the evidence at hand.

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