DSRT 734: From a Research Question to Hypotheses and Study Design

Turn a broad problem into an analyzable question

A broad problem such as employee turnover, patient wait time, or technology adoption is a topic, not yet an inferential question. An analyzable question specifies who or what the study concerns, what outcome will be measured, what comparison or relationship matters, and the population to which the researcher hopes to generalize. “Does a flexible schedule affect retention?” becomes more precise when it states the target workforce, the definition and time window for retention, and whether the study compares assigned schedules, chosen schedules, or existing groups.

Precision matters because each phrase creates a design obligation. A question about change within the same units implies linked measurements. A question about two separately sampled groups suggests independent observations. A question about prediction calls for a model and a defined outcome. The purpose is not to make the wording complicated; it is to expose the statistical target before the data are analyzed.

Distinguish the population from the sample

The population is the full set of units about which the research question seeks a conclusion. The sample is the observed subset that provides data. A sample result is a statistic; the corresponding population quantity is a parameter. Inferential reasoning uses the statistic and its sampling uncertainty to learn about the parameter.

Generalization depends on more than sample size. The sampling process, coverage, nonresponse, selection mechanism, and study setting affect whether the sample represents the intended population. A very large convenience sample can estimate its own participants precisely while remaining a poor basis for statements about a different population. Define the target population, accessible population, sampling frame, and actual sample separately so the boundary of the conclusion remains clear.

Identify outcomes, predictors, groups, and measurement types

The outcome is the response the analysis aims to explain, compare, or predict. Predictors are variables used to explain variation in that outcome. A grouping variable identifies conditions or categories whose outcomes are compared. These roles are contextual: the same measured variable can be an outcome in one research question and a predictor in another.

Variable type narrows the method family. Categorical variables place observations into categories, while quantitative variables record numerical amounts where arithmetic differences have meaning. Binary outcomes have two categories; counts are quantitative but often have distributional features that differ from continuous measurements. Coding a category with numbers does not turn it into a quantitative measure. Record the measurement unit, category meanings, possible range, and missing-value convention before analysis.

Decide whether observations are independent or paired

Independent observations do not have a natural one-to-one link across the groups being compared. Paired observations are connected within the same person, organization, matched unit, or repeated measurement. This distinction changes the unit of analysis and the uncertainty calculation. A paired comparison focuses on within-pair differences; an independent comparison focuses on differences between separate groups.

Dependence can also arise through teams, classrooms, hospitals, households, or repeated observations over time. Treating clustered observations as independent may make the analysis appear more precise than the design justifies. Use the data-collection process—not just the spreadsheet layout—to identify the actual observational unit and any pairing, nesting, or repeated structure.

Write null and alternative hypotheses about a population quantity

A null hypothesis usually represents a reference value, no difference, or no association for a population parameter. The alternative identifies the competing population claim. Hypotheses should be written before the result is interpreted and should refer to a parameter rather than a sample statistic. For a comparison of two population means, a two-sided pair might be H0: μ1 − μ2 = 0 and HA: μ1 − μ2 ≠ 0.

The wording must match the design and question. If the outcome is a proportion, mean notation is inappropriate. If data are paired, the parameter may be the mean or median of within-pair differences rather than a difference between unrelated populations. Rejecting the null does not prove the alternative with certainty; it indicates that the observed result is sufficiently incompatible with the null model under the method and assumptions.

Use directional hypotheses only when justified in advance

A directional alternative states that a parameter is greater than or less than a reference value. A non-directional alternative allows a difference in either direction. Direction should follow the research question and a defensible prior rationale, not the direction observed after seeing the data. Choosing a one-sided test after inspecting results changes the error behavior and can exaggerate evidence.

A two-sided question is often appropriate when effects in either direction would matter. A directional question may be defensible when the opposite direction is scientifically or practically irrelevant and the choice is documented before analysis. Even then, report the observed estimate and uncertainty so readers can evaluate magnitude, direction, and precision rather than seeing only a threshold decision.

Separate observational from experimental designs

In an observational study, researchers measure exposures and outcomes without assigning the exposure. Such studies can estimate associations, but confounding and selection may provide alternative explanations. In a randomized experiment, assignment by chance can balance both observed and unobserved factors on average, strengthening causal interpretation when implementation, measurement, attrition, and analysis remain credible.

Random sampling and random assignment solve different problems. Random sampling supports generalization to a population; random assignment supports causal comparison between assigned conditions. A study can have one without the other. State exactly what was randomized, how units entered the study, and what deviations occurred. Do not upgrade an observational association to a causal effect merely because the p-value is small.

Illustrative example: a fictional professional-development study

Suppose a fictional research team asks whether a structured mentoring program is associated with lower first-year turnover among newly hired analysts. The target population is newly hired analysts in mid-sized firms. The binary outcome is whether the analyst remains after twelve months. The explanatory variable is participation in the mentoring program. The sample contains records from firms that voluntarily adopted the program and comparable firms that did not.

Because firms chose whether to adopt mentoring, this is observational rather than randomized. A possible parameter is the difference in twelve-month retention proportions between the two program conditions. The null hypothesis states that the population difference is zero; the two-sided alternative states that it is not zero. Firm culture, hiring practices, and labor-market conditions may confound the association, so even a precise difference would not by itself establish that mentoring caused the retention change. The data are invented for illustration and are not a B03 assignment or official course exercise.

Let the design constrain the conclusion

A result can be calculated correctly and still support the wrong conclusion. Generalization requires a credible connection between the sample and the target population. Causal language requires a design and assumptions that address alternative explanations. Measurement quality affects what construct the estimate represents. Missing data and attrition can change the analyzed population. Dependence changes uncertainty.

Before writing the conclusion, audit four boundaries: who is represented, what was measured, how conditions arose, and which sources of bias remain plausible. Then choose verbs that match the evidence. “Was associated with” is not weaker writing when the study is observational; it is more accurate. A narrow, defensible conclusion is more useful than a confident claim the design cannot support.

Research-design check before choosing a test

Confirm that the question identifies a population, outcome, and comparison or relationship. Record each variable’s role and type. Identify the observational unit and any pairing, clustering, or repetition. State how the sample was obtained and whether any condition was randomly assigned. Write the target parameter and hypotheses before reading the result. Finally, list the claims the design can and cannot support. This checklist is a planning aid, not an official DSRT 734 or B03 requirement.

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Related DSRT 734 resources

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