In the first part, we emphasis on the importance of experimental data as part of the scientific method and via a number of examples we illustrate the concepts systematic and random error. Next we develop in an intuitive manner the concept of probability density. We elaborate on the normal distribution, the central limit theorem and the propagation of errors.
With this basis we discuss the construction of confidence intervals for large datasets (z-statistic) and a small number of observations (t-statistics). In the chapter on statistical tests we show the use of the z- and t-statistic to compare experimental data under various conditions. We use the F-test to compare precisions of experimental data en the Q-test to remove outliers. In the last chapter the method of least squares fit of a straight line is introduced for the construction of calibration curves, its construction, evaluation and use.
In a second part, we apply the theory on data derived from the chemistry and physics lab.
For all of this, we use excel and matlab.