Avoiding climate change data cherry-picking

This past Saturday, a group of 85 scientists published a meticulous 459-page rebuttal (https://lnkd.in/edGFJ-iU) to a recent Department of Energy (DOE) report claiming that greenhouse gas emissions have had a negligible impact on Earth’s climate (https://lnkd.in/e8QfZuZC). The rebuttal provides a line-by-line response to specific claims in the DOE report based on the peer-reviewed scientific record, going so far as to identify specific references that were misleadingly cited to support a pre-conceived narrative. As far as peer review goes, this is the gold standard. For example, on the topic of extreme precipitation and climate risk, the rebuttal notes that the DOE report cherry picks references to the authors’ own papers, relying on spatially and temporally limited precipitation records that overlook clearly discernable recent trends in extreme rainfall. The rebuttal was prepared as an official public comment on the DOE report, and DOE officials have stated their intent to meaningfully engage with responses to their report. Whether they in fact follow through on this promise will help to reveal their true motives. (The views expressed here are mine alone and do not reflect an official position.)

🧪 How I Avoid Misleading Results in Research Paper Submissions Putting a research paper together? Avoid these pitfalls by following 6 key steps I use to ensure integrity, transparency, and credibility in every study: --- 1️⃣ Use Reliable Data Sources • Always collect data from peer‑reviewed, official, or validated databases like Scopus, PubMed, Google Scholar, WoS. • Steer clear of unverified websites, biased samples, or incomplete datasets. ✅ This is the foundation of trust in research integrity. --- 2️⃣ Apply Correct Methodology • Choose defensible statistics & research methods appropriate to your design. • Collaborate with domain experts or statisticians to catch potential flaws before they derail analysis. --- 3️⃣ Report ALL Results—not just the positive ones • Avoid hiding inconvenient data—share both significant and non‑significant findings. • Transparency builds scientific credibility and trust—don’t cherry‑pick. --- 4️⃣ Avoid Overfitting & Data Manipulation • Don’t tweak data or model outputs to force-fit your hypothesis. • Say no to practices like p‑hacking, cherry‑picking, or fabricating outliers. --- 5️⃣ Disclose Limitations Clearly • Be upfront about limitations: small sample size, bias risks, methodological constraints. • Explain how those limitations may affect your conclusions to contextualize your results honestly. --- 6️⃣ Follow Ethical Review & Peer Input • Submit your study for peer-review or advisor feedback. • Use plagiarism tools like Turnitin or Grammarly. • Declare any conflicts of interest or funding sources. --- ⚠ Pitfalls to Avoid • Selectively excluding data • Ignoring failed or null experiments • Misinterpreting statistical outputs • Falsifying or fabricating results --- ✨ Why does this approach matter? These principles help ensure that research withstands scrutiny, promotes reproducibility, and fosters credibility in your academic community. Quality over hype every time. --- 🧠 What’s your process for avoiding misleading results in research? Share a tip or a challenge you’ve faced below 👇

🚩 How To Flag Misleading and Dishonest Charts (https://lnkd.in/e9cB8r4E), a practical guide on how to spot misleading charts to communicate insights more accurately and more reliably — with plenty of examples and design guidelines to create honest charts. Kindly put together by Nathan Yau. 🚫 Charts aren’t merely a visual representation of data. ✅ Charts are visuals that have a specific job to do. ✅ Don’t cut bar chart baselines — always start at 0. ✅ Don’t expand the y-axis beyond the max value. ✅ Don’t choose narrow segments to highlight a point. 🤔 Beware of smooth operator as it often hides real data. 🚫 Correlation doesn’t mean causation: validate and verify. ✅ Don’t add time gaps in the timeline: it hides what happened. ✅ Avoid leading titles, as people use them to interpret data. We often think of charts as visual representation of data. But as Nick Desbarats says, charts are visuals that have a job to do — e.g. make people aware, take an action, find an answer, filter or look up values. To do that job well, they need to be honest. And if they don’t, they spread skewed and biased messages, fast. Charts combine visual encodings (e.g. color, area, position, direction, length, angle) with scales. If the data is scarce, visual encodings fill a space based on available data — against the scales we choose to use for it. If the scales are chosen unfairly, or the data is cherry-picked, charts tell a wrong story. Here are some of the common attributes of dishonest charts: 🎢 Slopes → Artificial steepness of lines suggests notable changes. 🚢 Damper → Values appear smaller if y-axis expands beyond max. 🍒 Cherrypicker → Choosing narrow segments to highlight a point. 🌊 Smooth operator → Avgs show patterns, but hide bumps in reality. 🗑️ Overbinner → Clumping data into general groups to hide diversity. 👀 Base Stealer → Shortened y-axis makes tiny differences seem large. 🦋 Probable Cause → Showing 2 things follow similar/opposing patterns. ⏰ Time Gap → Points in time are purposely selected, others left out. 🔥 Storyteller → Leads with narratives, then squeezes data to support. 📇 Descriptor → Words chosen to deflect or invite misinterpretations. Different design choices lead to different charts, along with different interpretations attached to them. And that interpretation is often linked to what a reader already knows, what they expect, or what they choose to believe. The purpose of a good chart is to make wrong interpretations less likely. Unfortunately, there are plenty of charts that intentionally invite wrong interpretations. So be careful in choosing the data set to rely on, check sources, and explore not only what is there, but also what is missing. As Nathan suggests, a single data set can represent infinite narratives, depending on the angle you look from. So be cautious about the story you are telling, and avoid common but dishonest attributes that always invite wrong conclusions. #ux #design

This guide maps the minefield of science reporting, splitting problems into unintentional errors (e.g., confusing correlation with causation, oversimplifying complex findings, ignoring study limitations or statistical significance) and deliberate manipulations (sensational headlines, cherry-picked data, publication-bias exploitation, even fabrication). It offers a practical checklist for journalists and scientists alike: provide context (methods, sample size, limitations), report uncertainty (effect sizes, confidence intervals—not just p-values), avoid hype and absolutist claims, disclose conflicts, and link to sources and datasets. The aim is simple but urgent—prevent the public from being misled and rebuild trust through accuracy, transparency, and responsible communication. #learningdispatch #strategy #BoardOfDirectors #BusinessStrategy #LeadershipDevelopment #ExecutiveCoaching #BusinessLeaders #LeadershipMatters