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A major goal of many intermediate physics labs is learning methods of data analysis. In our Modern Lab course we introduce these methods in a planned sequence, with labs explicitly designed to match the sequence, so that students learn increasingly more sophisticated methods as the semester progresses. The first lab has them investigate repeated measurements of a single quantity (the speed of electromagnetic pulses and speed of light) and introduces the concept of error propagation. In the second lab they use a functional relation (lambda vs. sinθ ), for calibration of a diffraction grating, using residuals to optimize the fit. Later labs introduce Gaussian and Poisson probability distributions, and Least-Squares fitting of functions (including non-linear minimization). In addition, we provide here a few examples of how either methods or experiments can be adapted in order to support a coherent sequence of learning.

Sequential Introduction of Data Analysis Methods in the Modern Lab