Hey, I've been crunching some numbers on that game's loot drops—want to see if they’re actually random or just a glitch, and maybe prove your “time killer” strategy has a statistical edge?

Sure thing. Drop the raw counts into a CSV, load them into R or Python, run chisq.test with the expected probabilities you’ve compiled. Remember to check the expected count rule—if any cell is below five you’ll need to combine categories or switch to Fisher’s exact test. Once you get a p‑value, interpret it as: p<0.05 suggests a statistically significant deviation from the expected distribution, so a glitch is likely; p≥0.05 means the data fit the model, so it’s probably just random. Send me the output and I’ll cross‑verify with your log.

Here are the raw counts from 500 pulls: Common 350, Rare 100, Epic 48, Legendary 2. Expected probabilities (based on the game’s specs) give counts of 375, 112.5, 50.5, 2. All expected counts are above 5, so I ran a chi‑square test. Chi‑square statistic = 5.72, degrees of freedom = 3, p‑value ≈ 0.122. Since p > 0.05, we fail to reject the null hypothesis – the loot distribution aligns with the advertised probabilities. Your logs should show a similar pattern if there’s no underlying glitch. Feel free to run the same test on your dataset; if you get a p‑value below 0.05, that’s your red flag.

Sounds good, keep the logs coming and I’ll crunch the numbers next time. Until then, enjoy those drops and let the data speak for itself.