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Generate null and alternative hypotheses for your study. Describe your research question, the variables you will measure and your design, and get 4 matched hypothesis pairs, directional and non-directional, each phrased in your own variables and falsifiable with the design you described.
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The research question, the variables and how you will measure them, the groups or conditions, and the design. Named variables in produce precise hypotheses out.
Each card shows an alternative hypothesis with its exactly matched null, labeled directional or non-directional, so the statistical commitment of each option is visible up front.
Pick the pair your theory supports, match your statistical test to it, and write it into your proposal or preregistration before the data arrive. That order is what makes it confirmatory.
Every alternative arrives with its precisely matched null hypothesis, because the pair, not the prediction alone, is what your significance test actually uses.
At least one of each in every run, so the one-tailed versus two-tailed decision is made deliberately, with the trade-off in front of you.
Hypotheses are phrased strictly in the variables you name. No invented constructs, no predicted effect sizes, no statistics you did not supply.
Each option is written so a possible result pattern could refute it with the design you described, which is the property that makes it science.
The scientific value of a hypothesis comes almost entirely from when it is written: before the data arrive. Stated up front, a hypothesis disciplines the whole study; it fixes what you measure, what test you run and what would count as being wrong. Reverse-engineered afterwards to fit the results, the same sentence is not a hypothesis at all, which is why preregistration has moved from nicety to norm across the empirical sciences.
Writing one well is mostly a matter of precision about variables. The classic student errors are hypotheses with unmeasurable constructs, predictions so hedged that no result could refute them, and alternatives whose null does not actually mirror them, so the statistical test answers a different question than the one asked. Generating hypotheses as matched pairs, with the variables named and the direction made explicit, is a structural guard against all three.
Hypotheses sit in the middle of a pipeline. Upstream, a research question determines whether your study is confirmatory enough to carry hypotheses at all. Downstream, the finished study gets compressed by the abstract generator and indexed by the keywords generator. And because tense conventions differ between the hypothesis you proposed and the results you report, the verb tense checker keeps methods and results in the right tense when you write up.
When the manuscript comes together, the ProofreaderPro editor proofreads the full document with tracked changes you approve line by line, so the writing meets the same standard as the design.
Name your variables and population; each alternative hypothesis arrives paired with its null.
Variables: ten minutes of daily mindfulness practice, and self-reported test anxiety in first-year university students.
The directional pair predicts which way the effect runs (lower anxiety), the right wording when prior evidence points one way; the non-directional pair predicts only a difference, the safer wording when the literature could support either direction. Each null states the exact absence its alternative asserts, because the null is what the statistical test actually tests.
From proposal to submission, the ProofreaderPro editor proofreads your complete manuscript with tracked changes you accept or reject line by line. Free to try.
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