Inferential Statistics

Service Description

Definition: Inferential statistics involve using sample data to make generalizations or inferences about a larger population. It applies probability theory to estimate, test hypotheses, and draw conclusions about unseen data or future observations based on limited samples. Purpose: To test hypotheses and determine whether observed patterns are statistically significant or occurred by chance. Key Focus: Estimating parameters, testing relationships, and making judgments about populations. Common Techniques: Confidence intervals Hypothesis testing (t-tests, chi-square, ANOVA) Regression and correlation analysis p-values and significance testing Example (in Lean Six Sigma): A Black Belt takes a random sample of product weights from a production line and uses inferential statistics to determine—at a 95% confidence level—if the population mean weight meets specification.