Thread by @quaesita

0/ Essential philosophy for #DataScience, a thread of 32 questions.

Grab a friend (virtually) and tackle these 32 essential questions (all with more than one reasonable answer) that every serious #data professional should answer for themselves.

#philosophy #rstats #statistics

1/ Is it possible to know anything at all?

2/ What does it mean to "know" something?

3/ How do you know that you know something?

4/ What is the difference between knowledge, truth, assumptions, opinions, and beliefs?

5/ What does it take to "justify" a belief? (Please don't embarrass the class by blurting out, "Make a scatterplot!")

6/ Does it make sense to talk about a shared reality?

7/ Is knowledge personal or social?

8/ Is persuasion philosophically justified?

9/ Is there something special about mathematical reasoning?

10/ Can pure logic create knowledge?

11/ What is a fact?

12/ Can sensory experience produce knowledge?

13/ Does someone else's sensory experience give you less knowledge than your own?

14/ What makes a source of evidence trustworthy?

15/ If logic and data point in opposite directions, which way do you go?

16/ Does every quantity you've measured really exist? ("Rate your happiness on a scale of 1 to 7!")

17/ Do all measurements quantify anything real?

18/ What makes categories valid?

19/ Does evidence apply across nonidentical semantically-related concepts? (When, if ever, is it valid to apply evidence about something called user happiness to make inferences about something called user satisfaction?)

20/ Is there a difference between knowing a theory is true and acting as though a theory were true?

21/ Is it possible to behave as though a theory were true without believing it?

22/ Can you make reliable predictions about something without understanding how it works?

23/ What makes science "scientific" or special?

24/ Is there a reason to trust scientists more than other people?

25/ Are scientists who apply statistical hypothesis testing adhering to the core principles of the scientific method, such as falsifiability?

26/ Can analytics produce facts?

27/ Are analytical "insights" facts? Are statistical conclusions facts?

28/ Is it ethical to work on a project that may harm your fellow human beings?

29/ Is it ethical to use your data skills to legitimize someone's actions in hindsight?

30/ Is it ethical to use statistics for persuasion?

31/ Is it ethical to share your analysis with people who will draw incorrect conclusions from it?

32/ Who is morally responsible for how your conclusions are used?

Perhaps a better use of question 17 is:
17/ What makes a measurement valid?