How do transparent choices make a result more reproducible?

For: Readers beginning data analysis or checking whether a reported result can be reconstructed.

A result is easier to check when another reader can follow the route from observations to the reported number. The route includes choices: which cases entered, how missing entries were treated, what was grouped, which model was fitted and which output was reported. Begin with a small R example whose commands are visible. The goal is to understand the calculation and make its dependencies explicit, rather than merely produce a similar-looking chart.

The TOP Guidelines distinguish disclosure, sharing and verification. Saying that code exists differs from making it available with enough information to use it, and both differ from an independent check. The framework covers study plans, materials, data, analysis code and reporting. Use those categories to locate the missing links in a published analysis rather than treating a repository link as a certificate of quality.

Computational reproducibility asks whether the same data and procedures yield the reported result. Replication asks a related question using a new sample. A result can be computationally reproducible yet rest on a weak measure or an unsuitable design. The TOP definitions keep these checks distinct and also describe approaches that compare reasonable alternative analyses. TOP verification practices and study types.

For an original exercise, make a small invented dataset containing group labels, numerical scores and two missing values. Write down the score definition, the missing-value symbol and the population the example pretends to represent. Keep the original table unchanged and create a separate analysis script. Let that script produce a frequency table, a labelled graph and a mean with its denominator. Record the software and package versions actually used. These are practical recording choices for the exercise, not a claim that the source videos supply a complete research workflow.

Now change one clearly stated decision, such as the boundaries used to group the scores. Save the alternative output and explain why it differs. Do not silently replace the first result with the more attractive one. The frequency, histogram and mean lectures make these choices visible in small calculations. Their value here is that the connection between a decision and an output is inspectable. Descriptive Statistics with R.

In a real study, an analysis plan written before examining outcomes helps readers distinguish intended tests from later exploration. Exploration remains useful when reported as such. The ASA emphasises that interpretation requires full reporting and transparency; a single selected p-value cannot summarise all the analysis paths considered. ASA statement. For sensitive data, describe access conditions and share what can appropriately be shared; transparency is not an instruction to expose participants.

Finally, ask someone with the necessary access to reconstruct the invented result from the files and instructions. Record what they actually reproduced and where they needed help. If no second person has performed that check, say that the materials are prepared for reproduction. Making the work checkable and successfully checking it are different completed actions.

Next step

Apply the same reconstruction checklist to one openly documented analysis, recording unavailable data or missing steps as unresolved.

Limits of this route

The video descriptions were checked against identified official-transcript excerpts, not complete viewing. An accessible official media endpoint is not evidence that every frame played. Teaching examples are not independent validation of the underlying scientific or clinical claims. The exercise is original; these videos teach component calculations, while TOP supplies the open-science framework. No independent reproduction of their R outputs is claimed.