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This book addresses the statistical treatment of experimental data, catering to those new to statistics and probability, as well as those seeking rigorous estimates of uncertainties in various disciplines, including meteorology, engineering, physics, chemistry, and life sciences. A basic understanding of differential calculus suffices for comprehension. Utilizing an innovative teaching approach, the text employs simple laboratory experiments to develop detailed statistical analyses directly from students' logbooks, making it accessible even to laypersons. The book aims to clarify the often confusing array of statistical definitions, formulae, and assumptions by adhering to the recently introduced ISO directives. It covers essential concepts, parameters, and test variables for modern experimental data treatment, including error, uncertainty, distribution functions, and their parameters. Each concept is linked to straightforward experimental scenarios, with numerical data analysis provided. The correlation of uncertainties with the error matrix is explored in depth. Hypothesis testing is thoroughly presented, starting from basic principles and advancing to analytical details. The book illustrates the application of tests for fitting experimental curves, including χ2, t, and F tests, as well as ANOVA, which is frequently used in life sciences.