It's incredibly easy to game Twitter's trending news algorithm
Twitter’s “Today’s News” section is a mix of real news, very minor stories (usually discussion of a random AI-related post), nonsense trends, and barely disguised marketing.
The algorithm behind it seems pretty easy to manipulate.
This trending topic revolves around an explosive DM warning of imminent 25% layoffs at a FAANG company:
Here is the original post, which comes from an account called Tech Layoff Tracker (@TechLayoffLover):
There is no reason to believe this post is real. The account, created this month (February 2026), made its first post 7 hours ago. The post in question was made 5 hours ago, or 2 hours after the account’s very first post. Of course, the account carries an utterly meaningless blue “verified” checkmark.
But despite all this, the news summary puts “Tech Layoff Tracker” right in the headline, as if it’s a known reliable source and not an account (most likely) created the same day as the summary itself!
…These academic journal AI policies aren't going to last
I recently came across the following policy on the submission page of an academic journal:
Use of Artificial Intelligence (AI) tools: One of the goals of Spectrum is to stimulate critical thinking and skill development among authors and reviewers alike. Spectrum discourages the submission of content generated by artificial intelligence (AI)-assisted technologies (such as chatGPT and similar tools). This includes tools that generate text, data, images, figures, or other materials, as well as tools that are used to summarize and synthesize sources. Authors should be aware that such tools are vulnerable to factual inaccuracies, biases, and logical fallacies, and may pose risks to privacy, confidentiality, and copyright.
If authors choose to submit work created with the assistance of AI tools, such use must be disclosed and described in the submission. The disclosure must include: 1) what system was used, 2) who used it, 3) the time/date of the use, 4) the prompt(s) used to generate the content, and 5) the content in the submission that resulted from use of AI tools. The output from the AI system should also be submitted as supplementary material. Authors must accept full responsibility for the accuracy and integrity of the submission. AI systems do not meet the criteria for authorship, and should not be listed as a co-author.
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Agentic engineering patterns · ↗ simonwillison.net
Simon Willison is building a library of posts covering best practices for using agentic coding tools like Claude Code and OpenAI’s Codex. The existing articles cover test-driven development (red/green—ensure tests fail before the change and succeed after it) and AI-assisted code walkthroughs.
Comparing the Claw-like agent ecosystem · ↗ clawcharts.com
Chrys Bader has created ClawCharts to track the popularity and growth of OpenClaw and its growing number of competitors.
I have an unused Raspberry Pi 4 4GB that I’ve been meaning to test one of these Claw-like personal agents on (locked down to prevent the security nightmare scenarios we’ve seen play out since OpenClaw took off).
OpenClaw is a bit of a resource hog (which is why so many people are running out to buy Mac Minis), so I’ve been looking at the list of lightweight competitors. There is no obvious reason to prefer one over the other, so I’ll probably go with the fast-growing ZeroClaw.
ZeroClaw offers OAuth connectors for OpenAI and Anthropic subscription plans, but presently neither company is clear on whether this usage is permissible or not. Anthropic recently blew up the OpenClaw community by updating their docs to specifically ban using OAuth outside of Claude Code. An Anthropic employee partially walked this back on Twitter, but there is still no clear statement whether this use case is permitted. Regarding the use of OAuth from OpenAI for OpenClaw (specifically, GPT Codex), Peter Steinberger, creator of OpenClaw, stated on Twitter: “that already works, OAI publicly said that”. No one can seem to find this public statement, but it’s worth noting that Steinberger himself is now an OpenAI employee. So, will you get banned for using your ChatGPT Plus/Pro or Claude Pro/Max subscriptions with OpenClaw? Nobody knows.
…LLMs automate the erosion of online anonymity · ↗ arxiv.org
Economist Florian Ederer linked a new preprint describing the creation of an automated LLM-based pipeline for linking anonymous users across datasets based on unstructured text written by or about them. Prof Ederer is himself famous for unmasking the IP addresses of users of the infamous (but influential) Economics Job Market Rumors message board, exploiting a flaw in how usernames were assigned to anonymous posters. For platforms not encoding a user’s IP address in their “anonymous” username, the LLM-based approach involves:
- Extracting structured features from free text
- Encoding extracted features to embeddings to compare to candidate profiles
- Reasoning using all available context to identify the most likely match among top candidates
- Calibrate the quality of match by asking the LLM to report confidence
I guess it’s only a matter of time before someone uses this strategy to unmask Reviewer 2. (Currently this is only possible if Reviewer 2 insists you cite all of the work of the brilliant Dr. X.)
Oral texts · ↗ havelock.ai
A major intellectual current in the post-social media age is the rediscovery of media theorists like Marshall McLuhan, Walter Ong, and Neil Postman, whose works seem incredibly prescient in the age of the Internet and the instantaneous and omnipresent mass communication it enables.
A particular sub-current of this trend is the return to orality, a culture rooted in the spoken rather than written word. Indeed, the vast majority of human history is defined by oral culture, and the world’s brief sojourn to the written tradition may have finally ended thanks to the Internet.
One of the most impressive projects to come out of this domain is Havelock.AI, a tool created by journalist Joe Weisenthal and entirely vibe coded with Claude. The tool analyzes text to give an “orality score” with supporting analysis. For example, qualified assertions are considered literate, whereas categorical statements are considered oral. The tool defines 68 oral/literate markers based on the framework of Walter Ong. It really is an impressive tool that I recommend checking out.
I plugged a few of my old articles into the tool and apparently my writing is very much rooted in the written tradition! (This post also scores as strongly literate.)
Film recommendation: Bugonia
I watched Bugonia (from director Yorgos Lanthimos) blind tonight, and I highly recommend it. The film is centred on a broken man who loses himself in conspiracy theories to cope with his tragic circumstances, but it’s also so much more than that. It features outstanding performances by Jesse Plemons and Emma Stone, as well an absolutely kidney shredding score.
Looking up the film for this post and I see it was nominated for Best Picture this year. I’m not surprised. Definitely a great watch, having known nothing about the film going in beyond the one sentence description.
Bugonia is available to stream on Amazon Prime in Canada and probably elsewhere.
The increasingly inevitable social media ban for kids · ↗ www.afterbabel.com
Jon Haidt writes on his Substack about the increasingly popular movement to ban social media for kids, following the implementation of Australia’s under-16 social media ban a few months ago.
A brief history of chocolate in the army · ↗ www.mcgill.ca
I’m almost a week late, but I enjoyed this Valentine’s themed article from Joe Schwarcz of McGill University’s Office for Science and Society giving a brief history of the use of chocolate in the army.
It turns out M&Ms were first sold to the U.S. Army during World War II. Canadians will of course be familiar with Smarties, a similar candy that was invented first.
Democratizing voice cloning scams · ↗ github.com
Jamie Pine has launched Voicebox, a new voice cloning studio built upon the open weight Qwen3-TTS model. The project is positioned as a free, local alternative to the well-known ElevenLabs voice generator. A short demo video is available.
Obviously, there are legitimate uses for voice cloning technology. But in practice, this will be used to enable AI impersonation scams and spam on a massive scale. The GitHub page for this release isn’t exactly encouraging on this front. Demo screenshots show voice clones of YouTuber Linus Tech Tips, Minecraft creator Markus “Notch” Persson, and deceased streamer twomad.
Make sure you have a secret passphrase set up with your family, since your voice is no longer uniquely your own.
Don't let AI do your thinking for you · ↗ blog.cosmos-institute.org
Here’s a thought-provoking article from Harry Law on “The last temptation of Claude”—the urge to outsource all of your thinking to AI (and remember, writing is thinking).
A common theme in the AI commentary I’ve been reading lately is the growing importance of taste. AI is sending the cost of creating “content” (articles, analyses, video, etc.) to zero, even as the attention to consume it all remains fixed. If we want to keep living in a world where AI serves us, we need—more than ever—the discernment to choose the questions worth asking.
As I put it in my Globe and Mail op-ed on AI and journalism a few years ago:
AI won’t replace the sort of journalism that holds power accountable, but it could certainly enhance it. After all, you can teach a machine to spot patterns, but you can’t force it to care about your community.
In the multiverse of forking paths · ↗ statmodeling.stat.columbia.edu
STRANGE: I went forward in time to view alternate modelling decisions, to see all the possible outcomes of the coming analysis.
STAR-LORD: How many did you see?
STRANGE: 14,000,605.
STARK: How many did we achieve statistical significance?
STRANGE: One.
Prof. Jessica Hullman recently wrote a piece on Andrew Gelman’s blog discussing the use of ‘multiverse analysis’, i.e., what if we could see the results of the many slightly different decisions we could have made when constructing a model. This problem is commonly known as the garden of forking paths—during an analysis, a researcher is forced to make many small, sometimes arbitrary decisions that can lead to a different result if another researcher tries to independently replicate the analysis. While usually an innocent and inevitable part of the modelling process, these ‘researcher degrees of freedom’ can also be manipulated to produce a desired result.
Prof. Hullman points out that multiverse analysis will only become salient as AI coding tools such as Claude Code make it easier than ever to iterate on how we model our research questions.
Her longer paper with Julia M. Rohrer and Andrew Gelman, “What’s a multiverse good for anyway?” is available here.
Regulatory uncertainty threatens biotech innovation · ↗ www.clinicaltrialsabundance.blog
Another post from the Clinical Trials Abundance blog, this time by Ruxandra Teslo, on how the recent refusal-to-file by the US FDA for Moderna’s new mRNA influenza vaccine increases regulatory uncertainty and threatens innovation across the entire biotechnology sector. The decision reportedly came after the country’s top vaccine regulator, Dr. Vinay Prasad, overruled career staff to quash Moderna’s application. This is just one more blow against mRNA vaccine technology to come from Health and Human services, the US federal health agency led by the world’s most prominent antivaxxer, Robert F. Kennedy Jr.
US Medicaid data gets DOGE'd · ↗ opendata.hhs.gov
The US Health and Human Services DOGE team (I guess DOGE still exists in some form) just released a new aggregated, provider-level Medicaid claims database covering January 2018 through December 2024. With this dataset, you can track the monthly claims for each procedure (by HCPCS Code) and provider over time.
Even if the framing around this dataset’s release is partisan—tied to allegations of Medicaid fraud in Minnesota—it is a genuine advance in transparency for the US’s third largest spending program. No doubt this accomplishment required a lot of work on the backend to harmonize countless fragmented datasets into one tidy schema. These data were difficult to access before, and now they are free for anyone to use. Journalists, policy researchers, and companies working in the US healthcare sector will benefit the most, but every taxpayer benefits from added transparency about where their tax dollars go.
I would say there is the potential for these data to be misused to spark witch hunts, but this is more or less the stated purpose for this data release. Per Elon Musk: “Medicaid data has been open sourced, so the level of fraud is easy to identify.” If you go on Twitter, you will find several people have already plugged in the dataset to Claude Code and trumpeted their ASCII tables of providers flagged for potential fraud. Inevitably, some of these providers targeted by public scrutiny for their unusual billing patterns will have perfectly innocent explanations. But if ProPublica is excited about the release of this new dataset, then so am I.
More on vibe researching · ↗ joshuagans.substack.com
To follow on yesterday’s post on AI-produced research, here is a reflection on “vibe researching” from Prof. Joshua Gans of the University of Toronto’s Rotman School of Management. Since the release of the first “reasoning” models in late 2024, he has gone all in on experimenting with AI-first research.
One of the key takeaways is that he found himself pursuing low quality ideas to completion more often, precisely because the cost of choosing to continue to pursue a questionable idea has been lowered. Sycophancy is a problem, too. With an AI cheerleader, it is easy to convince yourself you have a result when you do not.
Those ideas were all fine but not high quality, and what is worse, I didn’t realise that they weren’t that significant until external referees said so. I didn’t realise it because they were reasonably hard to do, and I was happy to have solved them.
I will note that (human) peer reviewers cannot be the levee that stops the flood of middling AI research: the system of uncompensated labour that undergirds all of academic publishing is already strained to bursting, as every editor desperate to find referees for a paper will tell you.
Prof. Gans concludes his year-long experiment in “vibe researching” was a failure, despite publishing many working papers and publishing a handful of them:
…An end-to-end AI pipeline for policy evaluation papers · ↗ ape.socialcatalystlab.org
Prof. David Yanagizawa-Drott from the Social Catalyst Lab at the University of Zurich has launched Project APE (Autonomous Policy Evaluation), an end-to-end AI pipeline to generate policy evaluation papers. The vast majority of policies around the world are never rigorously evaluated, so it would certainly be useful if we were able to do so in an automated fashion.
Claude Code is the heart of the project, but other models are used to review the outputs and provide journal-style referee reports. All the coding is done in R (though Python is called in some scripts). Currently, judging is done by Gemini 3 Flash to compare against published research in top economics journals:
Blind comparison: An LLM judge compares two papers without knowing which is AI-generated Position swapping: Each pair is judged twice with paper order swapped to control for bias TrueSkill ratings: Papers accumulate skill ratings that update after each match
The project’s home page lists the AI’s current “win rate” at 3.5% in head-to-head matchups against human-written papers.
Prof. Yanagizawa-Drott says “Currently it requires at a minimum some initial human input for each paper,” although he does not specify exactly what. If we look at initialization.json that can be found in each paper’s directory, we see the following questions with user-provided inputs:
- Policy domain: What policy area interests you?
- Method: Which identification method?
- Data era: Modern or historical data?
- API keys: Did you configure data API keys?
- External review: Include external model reviews?
- Risk appetite: Exploration vs exploitation?
- Other preferences: Any other preferences or constraints?
The code, reviews, manuscript, and even the results of the initial idea generation process are all available on GitHub. Their immediate goal is to generate a sample of 1,000 papers and run human evaluations on them (at time of posting, there are 264 papers in the GitHub repository).
…There is only one statistical test · ↗ allendowney.substack.com
A classic article by computer scientist Allen Downey on why there is only one statistical test: compute a test statistic from your observed data, simulate a null hypothesis, and finally compute/approximate a p-value by calculating the fraction of test statistics from the simulated data exceeding the test statistic from your observed data.
Downey suggests using general simulation methods over the canon of rigid, inflexible tests invented when computation was difficult and expensive.
Hat tip to Ryan Briggs on Twitter.
The case for sharing clinical trial data · ↗ www.clinicaltrialsabundance.blog
Saloni Dattani of the excellent Works in Progress magazine (and formerly of Our World in Data) launched a new Substack today called The Clinical Trials Abundance blog. The first post is on the case for sharing clinical trial data. We have been gradually moving toward mandatory reporting of clinical trial results (though enforcement is another question), but sharing data would be one step further. Even though clinical trials rely on the trust (and often money) of the public, it can be very difficult to gain access to the raw results, even if journal article authors claim they are “available upon request”. A norm of clinical trial data sharing would not only increase the confidence in published results but also aid future drug development, reduce expensive redundancy, and improve meta-analyses (which are often forced to rely on heterogeneous summary measures).
Why a Canadian news site just launched an AI publishing tool · ↗ thehub.ca
It’s no secret that Canadian journalism (like journalism everywhere) is in trouble. Newsrooms face a steady stream of layoffs despite a couple hundred million Canadian dollars of direct and indirect government subsidies every year. The vast majority of outlets eligible for these subsidies take advantage of them, and combined they can subsidize half of a journalist’s salary. News organizations are desperate to diversify their revenue streams.
The Hub is a right-leaning publication launched in 2021 with a focus on policy and politics. Notably, the outlet declines or donates their subsidies, citing a valid concern that the scale of such subsidies threaten the perceived trustworthiness and independence of the media.
In late January 2026, The Hub launched NewsBox, an AI-powered publishing tool. NewsBox aims to make it easier for creators to transform their content (written, audio, or video) into other formats, such as speeches, essays, or talking points, while maintaining the author’s distinct voice. You can see examples of the tool’s output on new articles in The Hub, each of which is accompanied by an AI-generated summary and list of quotes at the top of the page. There is also a “Hub AI” chatbot in the sidebar of every article.
The app very much uses The Hub’s branding, prominently featuring the outlet’s co-creators, who also created NewsBox. While their pitch talks about preserving creators’ voices to avoid the “soulless prose” and “slop” outputted by ChatGPT and similar tools, I have to wonder if tighter integration of AI into the news and opinion side of the operation will raise its own issues with trust. The Hub has always been fairly tech-friendly, including a longstanding sponsorship by Meta.
…A handful of composers created most classic RPG soundtracks
I’ve always been a big fan of soundtracks, and video game soundtracks are no exception. Buying games on GOG.com usually nets you the soundtracks as well, so recently I’ve been enjoying a lot of classic RPG music. What struck me was how few composers were responsible for creating the ambiance of so many beloved classics. Look at how many series are covered by just the following six composers:
- Inon Zur (Icewind Dale II, Dragon Age: Origins, Dragon Age II, Fallout series starting with Fallout 3 plus Fallout Tactics, co-composer for Baldur’s Gate II: Throne of Bhaal and Pathfinder: Kingmaker, additional music for Neverwinter Nights)
- Jeremy Soule (Neverwinter Nights, Icewind Dale, The Elder Scrolls series starting with Morrowind, Star Wars: Knights of the Old Republic)
- Justin E. Bell (Pillars of Eternity series, Tyranny, The Outer Worlds)
- Mark Morgan (Fallout, Fallout 2, Planescape: Torment, Torment: Tides of Numenera, Wasteland 2, Wasteland 3)
- Kirill Pokrovsky (Divinity series up through Divinity: Original Sin)
- Borislav Slavov (Divinity: Origin Sin II, Baldur’s Gate 3)
Of the above, I highly recommend the truly excellent Divine Divinity soundtrack (terrible title, great music!), as well as Baldur’s Gate 3, particularly the vocal songs like “Down by the River”, “I Want to Live”, and “The Power”.
To me, it emphasized just how hard it is to break into this industry commercially, as these famous names and a handful of others will (deservedly!) continue to get work on the small number of major projects that get published every year. I worry that the less prestigious work that helps pays the bills/build experience for the large majority of composers who have yet to achieve name recognition will increasingly go to AI, impoverishing the pipeline for tomorrow’s great video game soundtrack composers.
How do you regain access to your computer if you lose your memory? · ↗ news.ycombinator.com
I read this interesting discussion this morning on Hacker News on the question of how to regain access to your computer if you lose your memory. As always, it starts with figuring out your threat model and responding accordingly.
Anthropic's statistical analysis skill doesn't get statistical significance quite right · ↗ github.com
Anthropic’s new statistical analysis skill demonstrates a common misunderstanding of statistical significance:
Statistical significance means the difference is unlikely due to chance.
But this phrasing isn’t quite right. The p-value in Null Hypothesis Significance Testing is not about the probability the results are “due to chance”; it is the probability—under the null hypothesis and the model assumptions—of observing results at least as extreme as the ones we obtained. In other words, the p-value summarizes how compatible the data are with the null, given our modelling choices. What it does not tell you is the probability that the null hypothesis is true.
Statistician Andrew Gelman gave a good definition for statistical significance in a 2015 blog post:
A mathematical technique to measure the strength of evidence from a single study. Statistical significance is conventionally declared when the p-value is less than 0.05. The p-value is the probability of seeing a result as strong as observed or greater, under the null hypothesis (which is commonly the hypothesis that there is no effect). Thus, the smaller the p-value, the less consistent are the data with the null hypothesis under this measure.
As some of the commenters in this blog post observe, simply being able to parrot a technically accurate definition of a p-value does not necessarily make us better at applying statistical significance in practice. It is certainly true that statistical significance is widely misused in scientific publishing as a threshold to distinguish signal from noise (or to be fancy, a “lexicographic decision rule”), which is why some scientists have argued that we should abandon it as the default statistical paradigm for research.
…The CIA World Factbook has been memory holed · ↗ simonwillison.net
Another staple of my childhood is gone, this time the CIA’s World Factbook. I have fond memories of consulting the World Factbook for school projects in my elementary school computer lab. But as of yesterday, the entire publication along with all of its archives have been suddenly and unceremoniously wiped from the agency’s website. At least archives of the website are still available on the Internet Archive, with complete zip files up to 2020 and Wayback Machine snapshots thereafter.
Guinea worm one step closer to eradication · ↗ www.cartercenter.org
Only 10 cases of guinea worm were reported in 2025, down from an estimated 3.5 million cases per year when the elimination campaign began four decades ago. The disease is an ancient one, believed by some to be the “fiery serpents” that beset the ancient Israelites in The Book of Numbers. It is treated by carefully wrapping the parasite around a small stick as it painfully emerges over the course of weeks. This may be the inspiration for the Staff of Asclepius (⚕), the predominant symbol of medicine showing a a serpent wrapped around a rod.
When I was studying mathematical modelling of infectious diseases at the University of Ottawa in the mid 2010s, the question was whether Jimmy Carter would outlive the guinea worm. Tragically, he did not, but his life’s work helped to prevent an estimated 100 million cases of the disabling disease and made him a hero in global health.
While we are within spitting distance of zero cases in humans, true eradication will be more difficult due to significant animal reservoirs of the disease. The press release notes nearly 700 reported cases in animals across six countries (and who knows how many unreported cases). These non-human reservoirs pose a significant barrier to true eradication, since the disease must die out not only in human populations but also in wildlife.
…msgvault: A personal email archive and search system to watch · ↗ wesmckinney.com
Here’s a new project to watch if you are interested in taking control of your email: msgvault. The tool provides a local, searchable version of all of your Gmail messages and attachments, backed by SQLite and DuckDB.
The author, Wes McKinney, says he may add support for other email services in the future, as well as WhatsApp, iMessage, and SMS. I’ll probably look into it for myself once the project matures a little. Although given that it stores everything in a single giant database file, it won’t fit into my standard backup strategy of versioned, incremental backups. Still, it could be a nice step forward in regaining control over my email archives.
Hat tip to j4mie on HackerNews.
The Divergent Association Task, a measure for creativity · ↗ www.pnas.org
The Divergent Association Task is a short, simple test introduced in 2021 claiming to measure creativity. Taking only a minute and a half, it asks participants to “generate 10 nouns that are as different from each other as possible in all meanings and uses of the words”.
Although the instructions say to “avoid specialized vocabulary (e.g., no technical terms)”, I imagine you might score higher if you’ve just finished cramming wordlists for the GRE. Researchers have used this test to compare human and AI creativity (though the use of GPT-4 in this article with a January 2026 publication date speaks to the incompatibility of AI research with traditional publication timelines).
A/B testing for advertising is not randomized · ↗ flovv.github.io
Florian Teschner writes about a recent paper from Bögershausen, Oertzen, & Bock arguing that online ad platforms like Facebook and Google misrepresent the meaning of “A/B testing” for ad campaigns. In A/B testing, we might assume the platform is randomly assigning users to see ad A or ad B, in an attempt to get a clean causal interpretation about which ad is more likely to drive a click (or whatever outcome you’re tracking).
But according to the paper, this is usually not what is happening. Instead, the platform optimizes delivery for each ad independently, steering each one toward the users most likely to click it. In other words, the two ads may be shown to different groups of users, and differences in click-through rates may be attributable to who is seeing the ad, as opposed to the overall appeal of the ad. Ad platforms convert A/B tests from simple randomized experiments into murky observational comparisons. For example, an ad may appear to do better because it happened to be shown disproportionately to a group with a high click-through rate, not because it presents a more compelling overall message. Advertisers get the warm glow of “experimentally backed” marketing without the assurances of randomization.
Total electoral wipeout · ↗ en.wikipedia.org
The 2002 Turkish general election is the canonical example of total electoral wipeout. Every party holding seats in the previous legislature was completely wiped out. Of the two parties that won seats in the 2002 election, the one that formed government didn’t even exist at the time of the previous election (current president Erdoğan’s AK Party, formed in 2001). Of note, it wasn’t a complete changing of the guard: one of the three independent members from the 1999 parliament won his seat again in 2002 (Mehmet Ağar), though it seems he took over as leader of one of the wiped-out parties shortly after the election.
Hat tip to kynakwado2 on Twitter.
Twyman's law · ↗ en.wikipedia.org
From Wikipedia:
Twyman’s law states that “Any figure that looks interesting or different is usually wrong”
A bit different from that oft-quoted line attributed to Isaac Asimov:
The most exciting phrase in science is not ‘Eureka!’ but ‘that’s funny’
But Twyman’s law is much truer in my experience. Surprising results are usually a signal that something is screwy with my data, my assumptions, or my pipeline.
Hat tip to DJ Rich on Twitter.
Remember that a lot of numbers are fake · ↗ davidoks.blog
David Oks wrote an essay reminding us that in many countries, even the most basic statistic—the population—is often shockingly uncertain or even outright fabricated. It’s a good reminder that many of the numbers we rely on for international comparisons, like crime rates and economic indices, are similarly troubled by incompatible definitions, uneven measurement, and varying degrees of manipulation. Ask Google what the population of Afghanistan is, and it will happily show you an annual timeline of population since 1960, but the tidiness of the chart belies the murkiness of the estimate.
One of the drawbacks of easily accessible international datasets from organizations like the World Bank and Our World in Data is that they paper over the huge differences among the underlying source datasets. Ultimately, you end up with one number from each country and the implication that they are all pointing to a single construct. This makes it far too easy to draw confident comparisons between countries that simply aren’t measuring the same thing. Without being forced to assemble these datasets yourself, it’s difficult to appreciate how messy it is to measure “the same thing” across different places (or even to measure the same thing over time within one place).
When evaluating a statistical claim, it’s always worth asking where the numbers come from and how they were measured. It’s easy to take figures at face value, especially when they’re rarely presented with any explicit uncertainty, which may be large. This goes double for more esoteric constructs like freedom scores or corruption indices, which often show up in social media posts cheerleading (or doom-mongering) one country over another. I remember one slickly produced video uncritically comparing COVID-19 statistics between Australia and Niger on the basis that they have the same population (do they?). Niger is one of the poorest and youngest countries in the world, and differences in demographics and health infrastructure alone invalidate any straightforward comparison with a wealthy Western country.
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