No black box between your data and the number.
You should never have to take a statistic on faith. Skari states which package computed each result, which assumptions were applied, and what happened to your data along the way — including the rows that were excluded and why.
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.
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The solution
Every step between your data and the result is named.
The package behind every number
Each analysis states exactly which R or Python package computed it — the same one you could go verify yourself.
What happened to your data
Excluded rows, imputed values, applied transformations — all stated, not silently absorbed into the result.
Self-verified code and documentation
Every method comes with code and documentation Skari has already checked against reference results — nothing is shipped unverified.