References

Cetinkaya-Rundel, M., & Hardin, J. (2021). Introduction to Modern Statistics. https://openintro-ims.netlify.app/

Deisenroth, M. P., Faisal, A. A., & Ong, C. S. (2020). Mathematics for machine learning. Cambridge University Press. https://mml-book.github.io/

Gelman, A., Hill, J., & Vehtari, A. (2021). Regression and other stories. Cambridge University Press.

Goodman, S. (2008). A Dirty Dozen: Twelve P-Value Misconceptions. Seminars in Hematology, 45(3), 135–140. https://doi.org/10.1053/j.seminhematol.2008.04.003

Hernán, M. A., Hsu, J., & Healy, B. (2019). A Second Chance to Get Causal Inference Right: A Classification of Data Science Tasks. Chance, 32(1), 42–49. https://doi.org/10.1080/09332480.2019.1579578

Ismay, C., & Kim, A. Y.-S. (2020). Statistical inference via data science: A ModernDive into R and the Tidyverse. CRC Press / Taylor & Francis Group. https://moderndive.com/

MacKay, R. J., & Oldford, R. W. (2000). Scientific Method, Statistical Method and the Speed of Light. Statistical Science, 15(3), 254–278. https://doi.org/10.1214/ss/1009212817

McElreath, R. (2020). Statistical rethinking: A Bayesian course with examples in R and Stan (2nd ed.). Taylor and Francis, CRC Press.

Pearl, J., Glymour, M., & Jewell, N. P. (2016). Causal inference in statistics: A primer. Wiley.

Poldrack, R. (2022). Statistical Thinking for the 21st Century. https://statsthinking21.github.io/statsthinking21-core-site/index.html

Roback, P., & Legler, J. (2021). Beyond multiple linear regression: Applied generalized linear models and multilevel models in (1st ed.). CRC Press.

Rohrer, J. M. (2018). Thinking Clearly About Correlations and Causation: Graphical Causal Models for Observational Data. Advances in Methods and Practices in Psychological Science, 1(1), 27–42. https://doi.org/10.1177/2515245917745629

Sauer, S. (2019). Moderne Datenanalyse mit R: Daten einlesen, aufbereiten, visualisieren und modellieren (1. Auflage 2019). Springer. https://www.springer.com/de/book/9783658215866

Wasserstein, R. L., & Lazar, N. A. (2016). The ASA’s Statement on p-Values: Context, Process, and Purpose. The American Statistician, 70(2), 129–133. https://doi.org/10.1080/00031305.2016.1154108

Wickham, H., & Grolemund, G. (2016). R for Data Science: Visualize, Model, Transform, Tidy, and Import Data. O’Reilly Media. https://r4ds.had.co.nz/index.html

Wild, C. J., & Pfannkuch, M. (1999). Statistical thinking in empirical enquiry. International Statistical Review, 67(3), 223–248.