Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is most often used by researchers to test predictions, called hypotheses. The first step in ...

Post-hoc testing is carried out after a statistical analysis where you have performed multiple significance tests, ‘post-hoc’ coming from the Latin “after this”. Post-hoc analysis represents a way to ...

Statistical testing in Python offers a way to make sure your data is meaningful. It only takes a second to validate your data. Having data is only half the battle. How do you know your data actually ...

P values indicate how incompatible the observed data may be with a null hypothesis; “P<0.05” implies that a treatment effect or exposure association larger than that observed would occur less than 5% ...

Aggregate supply equations are estimated using four alternative methods to segment the output price variable to measure short-run response of farm output to increasing and decreasing prices. Null ...

Hypothesis testing provides organizations with a structured approach to evaluate assumptions using data, reducing reliance on intuition and enhancing decision accuracy. By validating decisions with ...

In science and health, we are often looking for results that are considered to be “statistically significant.” The golden rule is if the p-value is less than 0.05, then the result is statistically ...