Academic English revision

We Measured 7 Ways to Explain Stats Simply. Five Failed.

Explaining stats in plain English is not a vocabulary problem. It is a rhythm and specificity problem, and the detector numbers prove it.

We ran an experiment you will not find in any writing guide or YouTube tutorial. Took one paragraph: a generic AI-written explanation of a mixed-methods statistical approach for an undergraduate research proposal. Ran it through two independent AI-text detectors. Baseline scores: 0.9997 and 0.9999. That is a flag on a flag. Then we applied six different 'make it sound human' techniques, one at a time. Same base paragraph. Same topic. No other variables changed. Here is what each technique did to those two numbers. And one result genuinely surprised us, because the fix everyone reaches for first, swap in fancier synonyms and upgrade your vocabulary, turned out to be the one that least matters to the second detector.

First, the thesaurus swap. We replaced plain verbs with their fancier cousins: 'analyze' became 'ascertain,' 'use' became 'utilize,' 'show' became 'demonstrate empirically.' detectorA dropped to 0.7202. Slight improvement. detectorB? 0.9998. Completely unmoved. Fancy vocabulary fools one scoring model and is invisible to the other. You spent twenty minutes upgrading every word in the paragraph and one tool blinked while the other did not even register the change. Now transition-word bloat. We stuffed 'furthermore,' 'additionally,' 'moreover,' 'consequently' between every single sentence. detectorA: 0.9691. You added four transition adverbs and moved the needle by three thousandths. The paragraph still reads like a robot that was told to 'connect its sentences.' The lesson: if your only move is decoration, you are decorating a flag. The flag is still there.

What actually moved numbers? Burstiness, mixing four-word fragments with thirty-word winding sentences. detectorA hit 0.4715, the best single-technique score in the entire set. But detectorB sat at 0.793. One tool impressed, the other shrugged. Deliberate rhythm, short-long-short beats like a drum pattern: detectorA 0.6818, detectorB 0.5157. More balanced, but neither under 0.5. First-person plus one concrete detail, 'I chose a two-way ANOVA because my advisor ran it on the pilot data and the interaction term was ugly': both detectors landed around 0.60. The honest read from this table: no single trick gets you under 0.5 on both tools simultaneously. You need to stack two or three before the paragraph stops looking like a template.

Then we ran the raw baseline, the 0.9997 and 0.9999 paragraph, through easydue strong mode. detectorA: 0.2057. detectorB: 0.009. That is not a nudge. That is the paragraph becoming unrecognizable to the scoring model. And it did not do it by swapping your vocabulary or padding with 'furthermore.' It restructured sentence rhythm, planted one or two concrete specifics, broke the uniform 18-to-22-word cadence that AI defaults to. If you are staring at a 2,000-word methods section at 1 a.m. and every sentence is exactly the same length, easydue is the thing you run before you run the detector. It is not magic. It is a structural edit that happens to make the pattern look human.

Now the actual writing problem, because the numbers only matter if you fix the paragraph. Undergraduate research proposal. Methods section. You need to explain a two-way ANOVA or a logistic regression to a committee that might be half engineers and half people who have not opened SPSS since 2019. The mistake you keep making: you mirror the textbook. You write 'A two-way analysis of variance was conducted to examine the interaction effect of gender and dosage on cortisol response levels.' Nobody in that room thinks in that syntax. They think: 'Okay, so we are comparing two groups across two conditions and seeing if the combo matters.' Say that first. Then name the test. The order is everything.

Here is the pattern. Explain the WHY before the WHAT. Give the reader a reason to care before you name the test. Three rewrites. ❌ 'A multiple linear regression was employed to ascertain the degree to which sleep duration and caffeine intake predict reaction time.' ✅ 'We want to know if sleep and coffee actually change how fast you react, or if the effect is mostly noise. A multiple linear regression lets us separate those two things while holding the other variables flat.' ❌ 'The study utilizes a mixed-methods design incorporating both quantitative survey data and qualitative interview transcripts.' ✅ 'Half the data is numbers from a 40-item survey. The other half is 12 recorded interviews where people explain their own numbers. We call that mixed-methods because we need both to answer the question.' ❌ 'Effect size will be reported alongside conventional significance thresholds to ensure interpretive transparency.' ✅ 'We will report the effect size, not just the p-value. A tiny effect with a clean p below .05 is not a finding. It is a small number that happened to clear the bar.'

Your committee does not grade your stats paragraph on whether you used the right test name. They grade it on whether you made them feel like the choice was obvious in hindsight. That is the difference between 0.9999 and 0.009. Not vocabulary. Not transition words. Rhythm, specificity, and the courage to write 'I chose X because Y' instead of 'X was selected to examine Y.' If you are two days from a proposal deadline and your methods section still reads like a textbook appendix, run it through easydue, then read it out loud. If any sentence makes you sound like you are reading a lab manual to a stranger, rewrite that one sentence. That is the whole job.