03 · What You Need to Know
How Generative AI Can Help Develop and Evaluate Competing Hypotheses
Alternative Research Hypotheses Are Not Necessarily Statistical Alternative Hypotheses
The word "alternative" has different meanings in scientific reasoning and statistical testing. This distinction is particularly important when prompting generative AI, because a model may respond with H0 and H1 even when you intended to explore competing scientific explanations.
Alternative or competing research hypotheses
Different substantive propositions that may explain a phenomenon or predict different relationships, patterns, or outcomes.
Statistical alternative hypothesis
A formal statistical claim, often denoted H1 or Ha, specifying parameter values or model conditions opposed to a statistical null hypothesis.
For example, one research hypothesis might propose that AI-assisted feedback improves students' writing confidence. A competing hypothesis might propose that extensive dependence on AI assistance reduces confidence in independent writing.
These are competing substantive predictions. They should not be confused with the statistical alternatives used to test a parameter.
When needed, AI can help distinguish research hypotheses from statistical hypotheses before the researcher proceeds to formal testing.
AI Can Challenge the First Explanation That Comes to Mind
Researchers naturally develop expectations from their theoretical commitments, previous findings, and disciplinary experience. Those expectations are useful, but they can also direct attention toward evidence consistent with the preferred explanation.
Generative AI can be used as a structured source of counterarguments. Instead of asking it to support the original hypothesis, ask what other explanations could produce the same observations.
For instance, a positive association between AI use and writing confidence could be consistent with AI assistance increasing confidence, confident students being more willing to experiment with AI, or both variables being influenced by prior technological experience.
These explanations are not necessarily equally credible. Their value depends on theoretical plausibility, empirical support, and whether the proposed design can distinguish among them.
Different Alternatives Challenge Different Parts of the Original Hypothesis
AI-generated alternatives may concern the direction of a relationship, its underlying mechanism, the relevant population, the conditions under which it occurs, or the possibility that the apparent relationship is explained by another variable.
| Type of Alternative |
Illustrative Proposition |
What It Challenges |
| Opposite direction |
More frequent AI use is associated with lower independent writing confidence. |
The expected direction of the relationship. |
| Reverse causation |
Students with lower writing confidence tend to increase their AI use. |
The proposed causal direction. |
| Common-cause explanation |
Prior writing experience influences both AI-use patterns and writing confidence. |
Whether the observed association reflects a direct causal relationship. |
| Conditional relationship |
The association differs according to students' prior AI literacy. |
Whether the relationship is similar across contexts or groups. |
| Alternative mechanism |
Any effect of AI assistance on confidence operates through perceived task difficulty rather than feedback quality. |
The proposed explanatory process. |
| No meaningful relationship |
AI-use frequency has little or no substantively meaningful association with writing confidence. |
The assumption that a meaningful relationship exists. |
All examples are hypothetical. They are possibilities for examination, not established findings or automatically justified hypotheses.
Some alternatives may require additional variables, longitudinal evidence, experimental manipulation, or different analytical models. Generating them is easier than designing a study capable of evaluating them.
Competing Hypotheses Should Be Meaningfully Distinguishable
Two hypotheses are most useful as competitors when they imply different observable expectations under sufficiently specified conditions.
Consider two propositions: "AI feedback improves writing confidence because students receive immediate assistance" and "AI feedback improves writing confidence because students experience reduced uncertainty." These offer different possible mechanisms, but a study measuring only AI use and confidence may not distinguish them.
To investigate the mechanisms, the researcher may need appropriate evidence about feedback experiences, perceived uncertainty, temporal ordering, and other relevant factors.
AI can help identify what observations would differentiate the explanations. If both hypotheses predict the same result under the proposed design, they may remain competing theoretical accounts without being empirically distinguishable in that study.
Alternative Hypotheses Require Substantive Justification
Generative AI can generate many logically possible relationships. Yet logical possibility is not sufficient grounds for treating every suggestion as a serious scientific hypothesis.
Suppose AI proposes that students' preference for dark-mode interfaces moderates the relationship between generative AI use and academic writing confidence. Such a relationship might be imaginable, but its inclusion would require a substantive rationale and relevant evidence.
Without that justification, adding the variable risks turning the study into an accumulation of speculative relationships.
Ask AI to distinguish among alternatives supported by established theory, those suggested by empirical findings, and those that are exploratory conjectures. Then verify the claims against original scholarly sources.
AI Can Help Identify Reverse Causation
One especially useful challenge to a causal hypothesis is to consider whether the presumed outcome might instead influence the presumed cause.
For example, a researcher may expect that frequent generative AI use increases writing confidence. Yet students who already have high confidence might be more willing to experiment with AI, or students with low confidence might seek more assistance.
These possibilities illustrate that the direction of an observed association cannot be established from the wording of a hypothesis.
AI can help articulate alternative temporal relationships, but determining causal direction requires appropriate evidence and assumptions. A cross-sectional association alone will rarely resolve the issue.
Alternative Explanations May Involve Confounding
A confounding explanation arises when the relationship between an exposure and outcome is distorted by other factors under the relevant causal structure.
Suppose prior academic writing experience influences both students' likelihood of using AI tools and their writing confidence. The observed association between AI use and confidence might partly reflect that prior experience.
AI can suggest possible confounders, but it may also generate variables based on superficial associations. A variable should not be treated as a confounder simply because it is correlated with both the exposure and outcome.
Determining whether a variable is a confounder requires causal reasoning, including consideration of temporal relationships and the assumed causal structure.
Researchers can use AI to identify potential confounding variables, but their inclusion in a causal model must be justified independently.
Alternative Mechanisms May Involve Mediators or Moderators
Some competing explanations concern how or under what conditions a relationship occurs.
A mediator is part of a proposed causal pathway between an exposure and an outcome. A moderator concerns variation in a relationship across levels of another variable or condition, although precise definitions depend on the analytical framework.
For example, AI-assisted feedback might influence writing confidence partly through students' perceived ability to revise their work. Alternatively, the relationship between AI use and confidence might differ according to prior AI literacy.
These are different kinds of propositions. AI should not label every third variable as a mediator or moderator without examining its theoretical role.
When such explanations are relevant, researchers may ask AI to identify candidate mediators and moderators while keeping their causal assumptions explicit.
More Alternative Hypotheses Do Not Automatically Improve a Study
Generative AI can produce dozens of hypotheses from a single research question. This may be useful during early exploration, but indiscriminate expansion can create theoretical incoherence and analytical problems.
Every additional hypothesis may require new measurements, data, assumptions, and analytical decisions. In confirmatory statistical research, multiple testing can also increase the risk of false-positive findings unless the analytical strategy addresses multiplicity appropriately.
A smaller set of theoretically meaningful and empirically distinguishable hypotheses is often more useful than a long list of speculative possibilities.
Ask AI to explain what unique scientific uncertainty each alternative addresses. Remove candidates that merely paraphrase existing hypotheses or introduce unsupported relationships.
Do Not Select Alternative Hypotheses After Seeing Which Results Are Significant
One serious research-integrity concern arises when hypotheses are formulated or selected after examining the results but presented as though they were specified beforehand.
Kerr described this practice as hypothesizing after the results are known, commonly abbreviated HARKing. The concern is not that researchers generate new explanations from data. Exploratory discoveries can be valuable. The problem is misrepresenting the sequence of scientific reasoning.
Generative AI makes it particularly easy to generate a convincing explanation for an observed result. A researcher could supply a surprising finding and ask the model to construct a hypothesis that appears to predict it.
Such explanations should be reported as exploratory or post hoc when that is how they arose. They may motivate future confirmatory investigation, but they should not be presented as independently corroborated predictions.
Watch Out
Do not use AI to generate numerous hypotheses, inspect which ones match statistically significant results, and then report only the successful predictions as though they were planned in advance. This can distort the apparent strength of the evidence and conceal the exploratory nature of the analysis.
AI Should Help Evaluate Alternatives, Not Declare a Winner
When presented with competing hypotheses, a language model may rank them according to apparent plausibility. Such rankings can be useful for identifying arguments, but they should not be treated as evidence that one explanation is true.
The relative credibility of competing hypotheses depends on the relevant literature, theoretical assumptions, research design, and empirical findings.
A productive prompt asks AI to identify the assumptions underlying each hypothesis, the evidence that would support or challenge it, and what the proposed study could realistically distinguish.
The goal is to improve the quality of scientific reasoning, not to replace empirical evaluation with a model's preferred explanation.
04 · A Practical Example
Using AI to Challenge a Hypothesis About Generative AI and Writing Confidence
Hypothetical Example
One observed association, several possible explanations
A researcher expects that frequent generative AI use is positively associated with academic writing confidence among undergraduate students. Before collecting data, the researcher asks AI to identify plausible competing explanations and explain what evidence would help distinguish them.
1. State the original hypothesis
"Generative AI-use frequency is positively associated with academic writing confidence among undergraduate students."
2. Generate competing explanations
AI suggests that AI assistance might increase confidence, that students' existing confidence might influence their AI use, or that prior writing experience might influence both variables.
3. Distinguish hypotheses from explanations
The researcher recognizes that the original hypothesis concerns an association, while the proposed alternatives include causal mechanisms and possible explanations for that association.
4. Identify distinguishing evidence
The researcher considers whether longitudinal observations, baseline confidence measures, prior writing experience, or an appropriate intervention design would help evaluate the competing propositions.
5. Assess feasibility and theoretical support
The researcher examines relevant literature and determines which alternatives are sufficiently justified and compatible with the available research design.
6. Preserve transparency
Any hypotheses adopted before data analysis are documented. Explanations generated after observing the results are identified as exploratory rather than retroactively presented as predictions.
The example shows that AI can make the researcher's assumptions more visible. It does not establish that any particular explanation is correct.
Importantly, an observational association may be compatible with several causal stories. If the study cannot distinguish them, the researcher should acknowledge that limitation rather than select the explanation that sounds most convincing.
