Reddit A/B Test Planning for Study Screening

Start with the Reddit experiment sample size planner. The worked example concerns completion of an external research screener, not a native Reddit A/B feature or a statistical comparison of organic post scores.

Choose a research decision smaller than community performance

Fictional Common Shelf Research is recruiting volunteers for a usability study about a community equipment library. It wants to improve the explanation shown before a short external screening form. One introduction describes the study’s purpose first; the other first explains the form’s four steps and then presents the same purpose. The rest of the screener is identical. Prospective volunteers arrive from a common Reddit recruitment route, meet the preassignment criteria, and receive a randomly chosen introduction in the research team’s external system. The question is whether the explanation order improves completed screeners, not whether one subreddit is a better audience or one public recruitment post is more popular.

The introduction is the only planned treatment difference. Both versions describe the same research activity, time commitment, participation conditions, and next step. They lead to the same questions and confirmation screen. If the team changes the reward, shortens the form, or offers a different study slot in B, it has changed the treatment into a package. That might be a worthwhile separate experiment, but the sample calculation would not isolate the introduction’s contribution. Preserve the exact versions with the plan so the final report can name the contrast. A narrow decision is easier to interpret than a broad claim that one approach works better on Reddit.

Read the surrounding community conversation to understand whether the invitation is clear and relevant, while keeping that context separate from experimental evidence. Comments can reveal questions about the task or the equipment-library concept. They do not form independent yes/no observations of screener completion. A single person can write several comments, and a quiet volunteer can complete the external form without commenting at all. Upvotes and post views likewise do not establish assignment to the two introductions. Use public responses as qualitative context or descriptive campaign observations. The randomized study begins at the external boundary where the team can actually define eligibility, assignment, and outcome measurement.

Keep communities from becoming accidental treatment groups

Suppose the team has an appropriate recruitment route in more than one relevant community. Assigning every arrival from one community to A and every arrival from another to B would confound introduction wording with source. Community members may differ in experience, familiarity with shared equipment, or willingness to join research. Instead, the external setup can randomly assign eligible arrivals from the declared recruitment population to either introduction, without making source determine treatment. Keep source information separately if needed to describe who enrolled. The planner models independent people at a stated allocation; it does not create a matched-community design, calculate cluster effects, or adjust for community-level differences.

The same person may participate in several communities and encounter the invitation more than once. Common Shelf therefore needs a credible participant identity process that prevents re-enrollment and retains the original assignment. A new campaign link does not create a new person. If the setup can distinguish only anonymous visits, acknowledge that limitation rather than labeling visits as volunteers. The independent-person sample size cannot correct overlap that the team does not measure. Forwarded invitations need the same consistent eligibility rule as direct arrivals. Decide those boundaries before launch so the final population is not quietly narrowed to whichever route produces the most favorable completion rate.

Keep the public recruitment explanation stable during enrollment where the design requires it. Revising the invitation halfway through the study could change the kind of volunteer who reaches both introductions, making the overall estimate refer to a mixture of recruitment conditions. Random assignment downstream can still serve a clearly specified question, but the changed context must be documented rather than ignored. Do not compare an early A cohort with a later B cohort as a substitute for simultaneous assignment. The calculator has no time-trend model, no community balancing procedure, and no way to infer that two separately recruited groups would have behaved similarly without the introduction change.

Separate preassignment eligibility from a completed screener

Common Shelf defines eligible entrants as new adult volunteers who report relevant equipment-borrowing experience through a common preassignment check and have not previously entered this study. That check is the same for everyone and occurs before either introduction is shown. The primary success is reaching the screener’s final confirmation with the required valid responses within five days of assignment. Each assigned person contributes at most one yes or no. A completed screener is not automatically an accepted study participant, a scheduled interview, or a useful research contribution. Those later decisions can be recorded separately without changing the primary binary outcome after the experiment begins.

Do not define the sample as only the people who finish the form. That would omit the noncompletions needed to estimate a completion rate and condition inclusion on the very behavior the introduction might affect. Similarly, removing people because their answers later make them unsuitable for a particular interview can change the estimand unless the rule belongs to a carefully specified design. In this example, preassignment eligibility defines who enters, and the same valid-completion rule determines the outcome in both arms. Downstream study selection does not retroactively erase an assigned person. The team must implement and document these distinctions outside the planner; the form fields merely describe the intended population and event.

The illustrative baseline completion rate is 20 percent. It is a fictional assumption for this particular screened population and five-day window, not a general Reddit conversion benchmark. Comparable prior recruitment could support a real baseline if its enrollment boundary, form requirements, and observation window match. Dividing completions by community subscribers or post views would answer a different question. If prior records cannot identify eligible assigned people, consider a separate measurement pilot rather than borrowing an attractive rate from an unrelated recruitment effort. Also inspect whether the four-step form and participation offer will remain stable long enough to make the planned experiment’s eventual result useful.

Reproduce the five-point improvement plan at 90 percent power

The research team decides that increasing completion from 20 to 25 percent would justify adopting the revised introduction. This is five percentage points and 25 percent relative lift. Choose Reddit, title the plan Common Shelf screener introduction, and enter the declared completion outcome and eligibility. Set baseline 20 percent, Percentage points 5, confidence 95 percent, power 90 percent, and Variant B share 50 percent. Use 70 combined eligible people per day, minimum enrollment 14 days, maturity 5 days, and start date 2026-11-23. The higher power choice is deliberate for this fixture; it is not a platform-specific setting or a rule that every Reddit study requires.

The core returns 1,464 people in each arm and 2,928 combined. Rounded achieved power is approximately 90.0057 percent under the stated approximation. At the assumed rates, expected completed screeners are 292.8 in A and 366 in B. These quantities are not promised interview candidates and do not imply that every completion will satisfy later study selection. The sample targets concern all eligible people assigned to an introduction, including those who never finish. Keep both arm counts in the launch record so a total-only enrollment update cannot conceal a shortfall in one arm or a departure from the chosen even allocation.

Ninety percent power means the modeled two-sided test detects the assumed five-point difference with about that probability when the specified alternative and model conditions hold. It is not the probability that the new introduction is better, and it is not a guarantee of a conclusive study. The method is a fixed-horizon normal approximation for two independent proportions, using pooled null variance and unpooled planned-alternative variance. It includes both rejection tails and checks the rounded counts. Repeated observations of the same person, assignment by whole communities, or repeated stopping decisions need different planning methods. The design declaration cannot certify that the external study avoids those problems.

Check whether the recruitment calendar still serves the study

At 70 combined eligible arrivals each day and a 50/50 split, each introduction receives a modeled 35 people daily. The 1,464-person target per arm requires 42 traffic days after rounding upward. This exceeds the 14-day minimum, so the plan uses 42 enrollment days. Starting November 23, 2026 places the inclusive enrollment end on January 3, 2027. Adding five maturity days produces January 8 as the review date and 47 total modeled days. The start date counts as the first enrollment day. Do not add another day simply because the sample division rounded upward or because the review occurs in a different calendar year.

The 70-person figure describes newly eligible entrants at the external assignment boundary every calendar day. It is not daily community visitors, post readers, or form sessions including returns. If recruitment is concentrated in a few bursts, that constant-rate model may be a poor description of the actual calendar. Compare the supplied assumption with a sustainable recruitment plan before reserving researchers’ time. The calculator does not predict how a community will respond or how long a post remains visible. Leaving traffic unknown removes the duration estimate while preserving sample targets; it does not justify importing an unrelated audience total just to make a review date appear.

A six-week enrollment period also raises a substantive question: will the study’s offer remain comparable throughout? Common Shelf should check whether equipment access, interview scheduling, and the form’s technical implementation will stay stable across the period. A planned review after the team’s research decision deadline is not useful merely because the calculation is correct. Adjust the research schedule or rethink the question before enrollment. The traffic and duration guide explains how allocation and minimum days affect the modeled calendar. Neither a longer minimum nor a later start automatically compensates for changes in who volunteers or what participation means.

Reserve five days for the last volunteers to finish

The five-day outcome window applies separately to each assigned volunteer. Someone who enters on January 3 needs the same opportunity to complete the screener as someone who entered in November. The maturity setting reserves time after enrollment for that final cohort; it does not add five days of new eligible people to the sample requirement. Establish exact operational timestamps outside the planner’s calendar-day fields. A form submission near the deadline should be handled by the same rule in both arms, not accepted or rejected according to which introduction it favors. Keep the final review contingent on outcome reconciliation as well as elapsed time.

Missing records need explicit attention. A person with no completed form by the end of an observed five-day window can be a noncompletion under the declared rule. A person whose event record is unavailable because measurement failed is not automatically the same thing. The external team must determine what can be established and report unresolved observation gaps. This calculator does not track assignment, recover lost form events, or classify missing outcomes. It simply models the binary event as supplied. Waiting until January 8 cannot prove that the data are complete, just as reaching 2,928 assignments cannot prove that those assignments represent independent people.

Operational monitoring during enrollment should focus on whether the experiment is functioning: both introductions load, the form accepts responses, assignment persists, and confirmation events are recorded. Such checks are different from repeatedly asking whether B has become statistically significant. The planner does not provide sequential stopping boundaries, adjust for repeated looks, or authorize changing the fixed horizon once an appealing difference appears. If the study needs an early-stop rule for inference, use an appropriate design before launch. Keep any necessary technical interruption and its consequences in the study record, including whether affected people remained in the population and how their outcomes were handled.

Use sensitivity without moving the research goalposts

At half the chosen effect, the target becomes 22.5 percent, or a 2.5-point improvement. Holding all other inputs fixed, the required sample is 5,625 people per arm and 11,250 combined. The supplied daily traffic implies 161 enrollment days, plus five maturity days for 166 total. From the same November start, review falls on May 7, 2027. That scenario may be incompatible with a study designed to inform a near-term service decision. The smaller effect could still matter, but this recruitment plan would have a much larger evidence commitment. Sensitivity exposes that mismatch without pretending the original population has more eligible volunteers than the team can actually recruit.

At twice the original improvement, the target is 30 percent. The model requires 392 people per arm and 784 combined, with 12 traffic days. The 14-day minimum then governs enrollment, followed by five maturity days, so review is December 11, 2026. That is a shorter schedule for a larger-effect question, not an alternative way to obtain the same evidence about a five-point gain. Pick the effect according to what change would alter the team’s decision. If a small improvement matters but cannot be measured feasibly, consider a longer-lived research process, better measurement, or clearly limited descriptive learning rather than an oversized claim from an undersized experiment.

Do not turn every community, recruitment date, device, or participant subgroup into a new winner comparison after enrollment. The sample plan concerns one predeclared two-arm question in the defined population. Descriptive source summaries can help explain who enrolled, but multiple inferential comparisons and treatment-effect differences across subgroups require their own planning. The tool does not adjust for those additional claims. Preserve exploratory observations as questions for later work rather than rewriting them as the original objective. The minimum detectable effect guide can help the team document a useful primary threshold before the recruitment effort makes changing direction costly.

Preserve a usable research handoff without overstating the result

Save the agreed planning project as editable JSON and keep the external recruitment and outcome records separately. The report CSV communicates calculated settings and scenarios, not raw volunteers or a Reddit export template. Copy results and downloads include the project title, eligibility, outcome, and Notes, and the PDF carries project JSON and report CSV attachments. Review those details before sharing a recruitment plan with collaborators. A short visible report does not remove sensitive context from its attachments. Rebuild after any input edit so the exported targets match the current assumptions. There is no autosaved study history, automatic enrollment feed, or participant identity matching supplied by this free planner.

After the fixed horizon and complete observation windows, analyze the actual data using a method appropriate to the design. The A/B results analyzer addresses supplied outcomes rather than before-test planning, and its requirements still need checking. State what was tested, who entered, actual arm counts, observation limitations, and deviations from the intended procedure. A higher completion rate alone is not proof that the underlying study recruits better interview participants or produces more useful findings. If the result remains uncertain, say that. The evidence concerns the introduction and the defined completion event among the people who reached this external test.

Use the conclusion to decide which screener introduction to retain and what to investigate next, without ranking entire communities from this experiment. For recurring account organization around recruitment, explore supported SMTasker workflows, while keeping volunteer allocation and research measurement in the external system. Assign responsibility for the final form, recruitment context, and future comparability checks. The free tools directory offers other resources for adjacent planning tasks. A strong handoff can be modest and concrete: a documented introduction choice, the evidence behind it, and a clear statement that comments, upvotes, and community membership were never treated as independent randomized outcomes in the sample calculation.

Questions about Reddit Study Test Planning

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