data description:

measures of uncertainty:

missing values:

statistical testing:

regression & correlation:

multivariate techniques:

spatial data analysis:

mean - median - mode, histograms, normality, outliers, modality,  box and whiskers plots, stem and leaf diagrams

sources of uncertainty, range, standard deviation, variance, inter-quartile range, error propagation

common problem in geology and generally ignored - real missing values vs. detection limits, and how to deal with missing values

hypotheses, confidence levels, value and rank testing, Z-, t-, Chi-squared, Kolmogorov-Smirnov, Mann-Whitney tests

Scatter diagrams, Pearson & Spearman correlation coefficients, significance of correlation, curve fitting, (non-)linear models

sum of squares methodology, discriminant function analysis, prin-ciple component & factor analysis, cluster analysis

spatial distribution of data, 3D visualization (isolines, bubble plots, trend surfaces), semi-variograms, kriging

All these aspects will be addressed both in the lectures and in practical sessions. However, you should also consult the book as it provides useful background, examples and more in-depth discussion of these subjects.

Copyright:     Vincent van Hinsberg & Simon Vriend

Last updated:     September 2025

AI and machine learning tools have the potential to transform (statistical) data analysis, making it faster and more effective, and allowing application of more diverse and advanced methods, thereby gaining a deeper understanding of your data.

However, AI is susceptible to hallucination and it can be hard to understand how it arrived at its answer. Moreover, results may not be robust or meaningful.

AI and machine learning are therefore a valid (and potentially powerful) aid in your data analysis, but you remain responsible for critically evaluating its output. Questions you should ask yourself include: Is the output correct; Is the output meaningful; Are fits and models robust; Do I understand how it arrived at the output/conclusions; Does the analysis meet data and method requirements; Is it the most appropriate method; etc.

Generative AI tools are therefore allowed in this course, including for the assessed Data analysis project, but any results obtained by using such tools must be attributed (e.g. type of tool and prompts used) and output has to be critically assessed (e.g. through method evaluation or test-set verification).

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