Know what is wrong before you run it.
Statistical engines fail late and explain little — a red error appears after the fact, and you are left guessing which column, which row, which assumption. Skari checks your data against the engine's real requirements first, and names the condition that failed.
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
Skari checks the data before it checks the code.
Pre-run validation
Your data and settings are checked against the method's real requirements before the analysis runs — not after it fails.
Named, specific failures
When something is wrong, Skari names the exact condition — a missing column, a wrong type, a violated assumption — instead of a generic error.
Cross-checked against reference output
Every method is validated against known reference results, so the numbers you see have already been checked once before you check them again.