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Model and Statistical Testing refers to the process of building mathematical or computational representations of real-world phenomena (models) and then evaluating their validity using statistical methods. A model simplifies complex systems by identifying relationships between variables, while statistical testing provides a framework to assess whether observed data supports or contradicts the assumptions made in the model. This involves formulating hypotheses, applying tests such as t-tests, chi-square, or ANOVA, and interpreting p-values or confidence intervals to determine significance. Together, modeling and testing ensure that conclusions drawn from data are not due to random chance but reflect meaningful patterns, making them essential tools in scientific research, data analysis, and decision-making.
This lecture is part of GEO 404 QUANTITATIVE TECHNIQUES IN GEOGRAPHY 2 in the Geology department. The course has 30 recorded lectures across 10 series, all free to watch.