What is the measure of whether a result is likely to be real rather than due to random chance?

• A. Dataset
• B. Regression Analysis
• C. API
• D. Statistical Significance

Answer: (D)

Statistical significance is about judging whether an observed result is unlikely to occur just by random variation under a baseline assumption of no real effect. In practice, you test a null hypothesis and compute a probability (a p-value) of seeing data as extreme as yours if there were no real effect. If that probability is very small—below a chosen threshold like 0.05—you declare the result statistically significant, meaning it’s likely reflecting a real effect rather than chance. It’s important to remember that significance doesn’t guarantee practical importance or causation, just that the observed pattern isn’t easily explained by random noise. The other terms aren’t measures of real-ness: a dataset is the data you analyze, a regression analysis is a method to model relationships (which may yield significant results), and an API is a software interface.