Statistical significance

Statistical significance is a term used in statistics to denote the likelihood that a relationship between two or more variables is caused by something other than chance. Statistical hypothesis testing is traditionally employed to determine if a result is statistically significant or not.

Definition

Statistical significance is used to reject the null hypothesis and is presented in terms of a level, typically a p-value. The smaller the p-value, the stronger the evidence that you should reject the null hypothesis. A p-value less than 0.05 (5%) is commonly referred to as statistically significant.

History

The concept of statistical significance was developed by Ronald Fisher in the early 20th century. His work has been influential in defining the modern concept of significance testing.

Misunderstandings

Statistical significance does not imply importance or practical significance. Many researchers have criticized the overuse of statistical significance in scientific research and have advocated for the use of effect size and confidence intervals instead.

See also

• Null hypothesis
• P-value
• Effect size
• Confidence interval
• Type I and type II errors

References

Statistical significance Resources
Need Help Finding a Doctor? Need help finding a doctor or specialist anywhere in the world? Explore our world directory. WikiMD's DocFinder can assist you in locating millions of physicians.	Medical Knowledge Access the latest research - Most recent articles Ongoing trials.

Translate to: East Asian 中文, 日本, 한국어, South Asian हिन्दी, Urdu, বাংলা, తెలుగు, தமிழ், ಕನ್ನಡ,
Southeast Asian Indonesian, Vietnamese, Thai, မြန်မာဘာသာ, European español, Deutsch, français, русский, português do Brasil, Italian, polski