Run it again anywhere. Get the same result.
A result you cannot reproduce is a result you cannot defend. Every analysis ships with the exact R or Python code behind it — take it to your own environment, re-run it, and submit it alongside your findings.
The problem
Statistics is hard even before you get an answer.
Doing it yourself in R or Python
Console ~/
Error in df$score * 2 : non-numeric argument to binary operator
…
Every package has its own syntax, and an error rarely says whether it's a typo or the wrong data.
| Group | Median | p |
|---|---|---|
| A | 840d | 0.013 |
| B | 529d | — |
p = .013 — but what does that actually mean?
A plot or table renders fine — now how am I supposed to interpret this?
# TODO: rename vars before submitting
x1 -> read.csv("untitled_final2.csv")
# remove debug print()
print(nrow(x1))
# delete the three commented-out attempts
# model2 -> lm(y ~ x1 + x2) # old
# add axis labels, a title, a caption…
Still has to be tidied up before it goes anywhere
The code still has to be cleaned up before it can be submitted anywhere.
1 / 2 — scroll to advance
The solution
The same code, the same data in, the same numbers out.
The exact code behind every result
Not a paraphrase or a hint — the literal R or Python code that produced the number in front of you.
Submission-ready, as it stands
The code is already organized enough to attach to a school or company submission — no separate cleanup pass required.
Same result, anyone's machine
Re-run the code in your own R or Python environment and you get the same numbers Skari showed you — that agreement is the proof.