How to get better at doing research [7 crazy simple tips]

Getting better at research comes down to running a repeatable loop: read deliberately, do experiments or analyses without fear of failure, then analyze the results in multiple formats until new insights reshape what gets read next. The practical payoff is faster learning—each cycle makes a researcher more attuned to what will work, what won’t, and where the real problems sit.

The first step is to treat reading as a scheduled, prioritized skill rather than background activity. The guidance is to read as much as possible at the start, get specific, and then gradually widen the scope. Blocking time on a calendar for reading may feel unproductive, but it’s positioned as foundational because it supplies the vocabulary, context, and prior art needed to design better questions and interpret outcomes.

After reading comes “doing,” with an explicit focus on overcoming the anxiety that shows up when hypotheses face reality. Testing ideas can be scary because results can be wrong, but mistakes are framed as essential learning signals. The instruction is to push past the “doing hump” and keep going—whether results fail, explode, or simply don’t match expectations—while still keeping the chaos under control.

Next is analysis, where the emphasis shifts from one-off interpretation to producing many outputs. The advice is to analyze in as many different ways as possible: generate graphs, schematics, and tables; use equations; and transform the same data into multiple views. Those outputs then feed back into the reading stage, restarting the cycle and sharpening future decisions.

Beyond the loop, research improvement depends on communicating understanding. Being able to explain research clearly—from a child to a professional—signals deep familiarity with the challenges, the vocabulary to describe them, and the ability to spot gaps. The guidance also stresses talking about research more often, not as a deep technical lecture but as a succinct, engaging explanation that invites curiosity.

A second structural principle is breaking big questions into smaller, researchable chunks. Anxiety becomes a diagnostic tool: if a question feels overwhelming or paralyzing, it’s likely too large. The “right size” is described as something that can be researched, experimented on, and analyzed within about a week or a couple of weeks (or up to a month if the field requires it). From there, the same read–do–analyze cycle can be applied at the smaller scale.

Clarity and focus are treated as ongoing habits. Asking “why” repeatedly during experiments helps uncover mechanisms and alternative paths, shifting attention from “how did I get this result?” to “why did it happen?” Time structure matters too: set blocks dedicated to the core activities and minimize distractions like grant work and meetings to the extent possible. Saying no to other requests is presented as a legitimate strategy.

Finally, writing and prioritization are positioned as accelerators. Writing—starting earlier than a “full story” is ready—reveals gaps and clarifies what experiments or chapters are missing. When results show promise, the advice is to double down on what’s working using an 80/20 mindset, while accepting that some efforts won’t pan out. The overall message: run more cycles, chase clarity, and invest time in the fundamentals that reliably turn uncertainty into progress.