Exploring the Intersection of Data Science and Philosophy!

> It is crucial for data scientists to carefully consider the quality and representativeness of the data they are working with

To bring this fully to the domain of epistemology, you could go further: why are certain markers of quality or measures of representativeness valid? If we develop a measure and it gives us a particular answer, can we tell whether the data or the measure is at fault.[1]

> [statistical significance] refers to the likelihood that a result or relationship observed in a sample is not simply due to chance, but rather reflects a genuine trend or pattern in the population.

This is in general not correct — statistical significance is about the probability of an observation assuming that the null hypothesis is true.[1] There is a narrow context in which you could interpret a p-value as being about the probability that there is a true effect: if you're doing Bayesian inference with a flat prior, but that is itself a very strong claim to make.

[0]: https://plato.stanford.edu/entries/measurement-science/#TheL... [1]: https://en.wikipedia.org/wiki/Misuse_of_p-values#Clarificati...

Data science and philosophy may seem like unrelated fields, but they are actually closely connected in many ways. This article explores the various ways in which data science and philosophy intersect, including through the concepts of epistemology, statistical methods and models, ethical decision-making, and ontology.

The article is kinda short, i ask you to read it or at least scroll down a bit. Otherwise the algorithm will punish my article

No coverage of autonoma? (or narrower view of field of logic, aka formal logic vs. electrical engineering logic)

Computational linguistics a 'data science', 'philosopy' or both?