What is Statistical Significance?

Ruben Buijs

Founder & Digital Consultant

Written on Aug 1, 2023

2 minutes

Lead generation

Statistical significance is a concept used in data analysis to determine whether a result is likely to be meaningful or if it could have occurred by chance. It helps researchers and marketers make informed decisions based on the data they collect.


Let's say you conduct an A/B test to compare two versions of a website landing page. You want to determine if changing the color of the call-to-action button from green to blue leads to a higher conversion rate. After running the test, you find that the blue button resulted in a 5% increase in conversions. But is this difference statistically significant?

To find out, you calculate the p-value, which measures the probability of observing a result as extreme as the one you obtained, assuming there is no real difference between the two button colors. If the p-value is below a predetermined significance level (often 0.05), you can conclude that the difference is statistically significant and not due to random chance.


Statistical significance is crucial because it helps distinguish between findings that are likely to be reliable and those that could be mere coincidences. By using statistical significance, researchers and marketers can confidently determine which variations or interventions have a genuine impact.

Without statistical significance, it would be challenging to identify the effectiveness of different strategies, such as testing new marketing campaigns, evaluating the impact of product changes, or assessing the success of lead generation techniques.

How to Use Statistical Significance

To use statistical significance effectively, follow these steps:

  1. Define your research question: Clearly articulate what you want to investigate or compare.

  2. Choose an appropriate statistical test: Select a test that aligns with your research question and the type of data you have (e.g., t-test, chi-square test, ANOVA).

  3. Collect and analyze data: Gather relevant data and apply the chosen statistical test to calculate the p-value.

  4. Set a significance level: Determine the threshold below which you consider a result to be statistically significant. The most common significance level is 0.05 (5%).

  5. Interpret the p-value: Compare the p-value to your significance level. If the p-value is less than or equal to the significance level, you can conclude that the result is statistically significant.

  6. Draw conclusions: Based on the statistical significance, make informed decisions and take appropriate actions.

Useful Tips

  • Ensure an adequate sample size: A larger sample size generally increases the power to detect statistically significant effects.

  • Understand the limitations: Statistical significance does not guarantee practical significance. Even if a result is statistically significant, consider the magnitude of the effect and its practical implications.

  • Replicate and validate: Repeating experiments or tests can help validate findings and ensure the consistency of results.

  • Consider the context: Statistical significance should be interpreted in the context of the specific research question and the field of study.

  • Hypothesis testing
  • Confidence interval
  • Type I error
  • Type II error
  • Effect size
  • Power analysis
  • Correlation analysis
  • Experimental design
  • Sampling techniques
  • Data visualization


Statistical significance is a measure used in research to determine if the results obtained are likely due to chance or if they are a true representation of the population being studied.
Statistical significance is typically calculated using statistical tests, such as t-tests or chi-square tests, which analyze the data and provide a p-value. If the p-value is below a certain threshold (usually 0.05), the results are considered statistically significant.
Statistical significance helps researchers determine if their findings are meaningful and can be generalized to the larger population. It provides evidence that the observed effects are not due to random chance.
Statistical significance focuses on determining if the results are unlikely to be due to chance, while practical significance considers the magnitude or importance of the observed effects. A study can be statistically significant but not practically significant if the effect size is very small.
Statistical significance does not guarantee that a study's findings are correct, but it provides evidence that the results are unlikely to be due to chance. Other factors like sample size, study design, and potential biases should also be considered.
If a study does not show statistical significance, it means that the observed effects are likely due to chance. Researchers may need to collect more data, refine their methods, or reconsider their hypotheses to obtain statistically significant results.
Not necessarily. While statistically significant results indicate that the observed effects are unlikely to be due to chance, practical importance depends on the context and the magnitude of the effect. Researchers should consider the practical implications of their findings.
Yes, sample size can influence statistical significance. Larger sample sizes provide more reliable estimates and increase the chances of detecting statistically significant effects, especially if the effects are small.
No, statistical significance and clinical significance are different concepts. Statistical significance focuses on the probability of obtaining the results by chance, while clinical significance relates to the practical importance or impact of the results on patient outcomes in a clinical setting.
Statistical significance is based on the available data and can change as more data is collected. With larger sample sizes or different study designs, the statistical significance of the results may vary.

Article by

Ruben Buijs

Ruben, the founder of Boei, leverages over a decade of consultancy experience at Ernst & Young to optimize lead generation. Boei specializes in converting website visitors into qualified leads. Outside of work, Ruben is passionate about crossfit and enjoys gaming occasionally.

Table of contents

  1. Examples
  2. Importance
  3. How to Use Statistical Significance
  4. Useful Tips
  5. Related Terms

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