TIM-6520

De Vires, S. (2017, January 5). 5 types of bias in data & analytics.
This resource highlights biases that should be avoided in conducting research.

Knüsel, B., Baumberger, C., Zumwald, M., Bresch, D. N., & Knutti, R. (2020). Argument-based assessment of predictive uncertainty of data-driven environmental models. Environmental Modelling and Software, 134.
This resource proposes a framework for uncertainty assessment that uses argument analysis to assess the justification of the assumption that the model is fit for the predictive purpose at hand.

Goosey, B. (2017, April 5). Check your assumptions about your data.
This resource discusses the importance of identifying and reviewing any assumptions one might have about the data one is working with.

Punia, S., Singh, S. P., & Madaan, J. K. (2020). From predictive to prescriptive analytics: A data-driven multi-item newsvendor model. Decision Support Systems, 136.
This resource proposes a distribution-free and completely data-driven solution approach to Z. The proposed approach uses sample demand data as input and machine (and deep) learning methods with empirical risk minimization principle to find order quantities.

Nielsen, S. F. V., Schmidt, M. N., Madsen, K. H., & Mørup, M. (2018). Predictive assessment of models for dynamic functional connectivity. NeuroImage, 171, 116–134.
This resource considers a predictive likelihood approach to model assessment, where models are evaluated based on their predictive performance on held-out test data.

APA Dictionary of Psychology. (2020). Statistical conclusion validity. Author.
This resource gives a definition of statistical conclusion validity.