Abstract
Probability and statistical methods are a better tool for making scientific inferences and handling uncertainty in empirical contexts. We show how the uncertainties happen in inferences and predictions, and how to handle them easily in some cases. Starting with a couple of probability paradoxes, for giving the reader an idea about how tricky the application of the probability can be, a potential uncertainty in statistical significance testing is shown. How the predictions can be done effectively with the probabilistic approach while handing the uncertainties is presented. And finally, what the analyst needs to consider when doing discrete predictions is discussed through application of Simpson’s paradox. For the task effective use of causal assumptions is discussed.
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Wijayatunga, P. (2023). Some Cases of Prediction and Inference with Uncertainty. In: Abraham, A., Hanne, T., Gandhi, N., Manghirmalani Mishra, P., Bajaj, A., Siarry, P. (eds) Proceedings of the 14th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2022). SoCPaR 2022. Lecture Notes in Networks and Systems, vol 648. Springer, Cham. https://doi.org/10.1007/978-3-031-27524-1_25
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