Today we will look at how decision making can be improved.

But before that, I wanted to share with you what you could unlock if you share Data Tinkerer with just 1 more person.

There are 100+ resources to learn all things data (science, engineering, analysis). It includes videos, courses, projects and can be filtered by tech stack (Python, SQL, Spark and etc), skill level (Beginner, Intermediate and so on) provider name or free/paid. So if you know other people who like staying up to date on all things data, please share Data Tinkerer with them!

Refer a friend

With that out of the way, let’s get to the today’s topic of decision making!

Last week I was reading the bitter lesson by Rich Sutton again. Great read, if you haven’t already. And it got me thinking: are there similar patterns in the world of data? Are there cases where we overvalue the human knowledge of the domain? And I think there is a close analogy in decision-making.

The biggest lesson that can be read from years of judgment and decision-making research is that simple, consistent rules beat human judgement most of the time.

This can be hard to accept because it feels like an attack on expertise but the evidence is blunt: human judgment is often noisy, inconsistent and overconfident. Paul Meehl made the argument in 1954 that statistical rules often outperform expert clinical judgment. Later reviews found the same pattern across many fields.

We like to believe that experience gives us a special ability to see the truth of a case. We like to believe that judgment lives in the rich details, the subtle signals, the human context. But often, when we test that belief against outcomes, the details we trusted were noise, the subtle signals were distractions and the human context gave us more confidence than accuracy.

Daniel Kahneman’s work helps explain why. Intuition is pattern recognition, not magic. It works when the environment is stable and feedback is fast and clear. Chess players can build good intuition because they see repeated patterns and receive immediate feedback. Many business, hiring, policy and strategy decisions are different. Feedback is delayed, messy and often ambiguous. A bad decision can look good because of luck. A good decision can look bad because the environment changed. In that kind of setting, intuition can feel like expertise while behaving more like confidence.

Hiring is a simple example. Managers often believe they can spot talent through conversation. They notice confidence, polish, energy and ‘fit’. But unstructured interviews are noisy. Two interviewers can see the same candidate and walk away with different conclusions. The same interviewer may judge differently depending on mood, fatigue or one memorable answer. A structured process feels less impressive: define criteria, ask similar questions, score each dimension and combine the scores. But that boring process is usually fairer and more predictive.

The same pattern appears in admissions, forecasting, insurance, performance reviews and risk assessment. Kahneman and Sunstein called this problem ‘noise’: unwanted variation in judgments that should be much closer together. In Noise, they describe an insurance company where underwriters independently priced the same fictitious cases and the median variation in premiums was 55%, far higher than executives expected.

Bias gets most of the attention because it has a story. Noise is harder to see because there is no obvious villain. No one needs to be irrational, corrupt or incompetent. People can be smart, experienced and honest and still produce wildly different judgments.

In AI, methods that scaled with computation beat methods that tried to encode human cleverness. In decision-making, methods that scale with evidence and consistency beat methods that try to preserve human intuition.

That does not mean algorithms should replace people. Humans still define the goal, decide what data is legitimate, handle ethical trade-offs and question whether history still applies. The lesson is narrower: stop using intuition for the parts of decision-making where tested, consistent rules do better. Use people to frame the problem but use algorithms for repeated decision making scenarios.

What are your thoughts?

If you are already subscribed and enjoyed the article, please give it a like and/or share it others, really appreciate it 🙏

Share

Keep learning

I’m continuing the series where we hear from people working in data - how they got here, what they’ve learned and what their day-to-day really looks like.

Meet who is a Senior Healthcare Data Analyst and writer of the Journal of a Data Analyst newsletter.

Journal of a Data Analyst

A journal-style newsletter exploring the data industry and careers - mixing insights, discussions, and practical knowledge.

By Thais Cooke

We talked about her unplanned pivot into data, what healthcare data analysis look like and how she thought she was being scammed by a LinkedIn ‘Impostor’.

So without further ado, let’s get into it!

Read more

Today we will look at how to strike a balance between speed and accuracy.

But before that, I wanted to share with you what you could unlock if you share Data Tinkerer with just 1 more person.

There are 100+ resources to learn all things data (science, engineering, analysis). It includes videos, courses, projects and can be filtered by tech stack (Python, SQL, Spark and etc), skill level (Beginner, Intermediate and so on) provider name or free/paid. So if you know other people who like staying up to date on all things data, please share Data Tinkerer with them!

Refer a friend

With that out of the way, let’s get to the today’s topic of deciding when good enough is good enough!

Read more

Before that, a quick note: based on feedback, I’ll be making changes to the round-up section of the newsletter. Going forward, I’ll focus more on longer-form pieces where you can learn something - less news, more depth. I’ll share more about this next week.

But for now, let’s tackle an issue most analysts eventually face - especially when job-hunting:

How do you show that your work actually made a difference?

Read more

Today we will look at how the alignment trap can keep you from delivering a fair analysis.

But before that, I wanted to share an example of what you could unlock if you share Data Tinkerer with just 2 other people.

There are 100+ more cheat sheets covering everything from Python, R, SQL, Spark to Power BI, Tableau, Git and many more. So if you know other people who like staying up to date on all things data, please share Data Tinkerer with them!

Refer a friend

Now, with that out of the way, let’s get to today’s data analysis article!

Read more

Today we will look at how denominator change can trick up your analysis.

But before that, I wanted to share an example of what you could unlock if you share Data Tinkerer with just 2 other people.

There are 100+ more cheat sheets covering everything from Python, R, SQL, Spark to Power BI, Tableau, Git and many more. So if you know other people who like staying up to date on all things data, please share Data Tinkerer with them!

Refer a friend

Now, with that out of the way, let’s get to today’s data analysis article!

Read more

We will be doing the first guest post which I mentioned in last week’s deep dive. Again, if you are a data person in the trenches, reply to the email or message me and we can work it out. We get almost 10k views every month and your experience and learnings would be valuable to others

As for today’s post, I came across post and found her reflections and assessment refreshing. She offered an honest and introspective look into freelancing without sugarcoating. I thought this would be an interesting perspective since I know there are a lot of folks in data who are thinking about freelancing. Her publication is below, check it out and subscribe if you want to learn more about data analysis.

Data according to me...

Analyst of data, my funny tech journey! We all have stories whether we want to share them or not. Join me on my data storytelling journey as a data analyst/freelancer. It's simple but not for the simple-minded. A guide to growing as a data analyst.

By Ame_data analyst

Now, without further ado, I hand it over to Amy to introduce herself and get into the article!

Read more

As I mentioned earlier this week, you can have access to 100+ cheat sheets covering everything from Python, SQL, Spark to Power BI, Tableau, Git and many more. You just need to share Data Tinkerer with 2 other people to unlock it

So if you know other people who like staying up to date on all things data, please share Data Tinkerer with them!

Refer a friend

Now, without further ado, let’s get into a fallacy you want to avoid!

Read more

As I mentioned earlier this week, you can have access to 100+ cheat sheets covering everything from Python, SQL, Spark to Power BI, Tableau, Git and many more. You just need to share Data Tinkerer with 2 other people to unlock it

So if you know other people who like staying up to date on all things data, please share Data Tinkerer with them!

Refer a friend

Now, without further ado, let’s get into a fallacy you want to avoid!

Read more

Thank you to those who provided feedback this week and to the 2 people who shared the publication with others. I really appreciate it ❤️

I wanted to share an example of what you could unlock if you share Data Tinkerer with just 3 other people.

There are 100+ more cheat sheets covering everything from Python, R, SQL, Spark to Power BI, Tableau, Git and many more. So if you know other people who like staying up to date on all things data, please share Data Tinkerer with them!

Refer a friend

Now, without further ado, let’s get into a data analysis issue you want to avoid!

You’ve launched a new A/B test: a green “Buy Now” button vs. the classic blue.

First check: p = 0.08
Second check: p = 0.06
Third check after lunch: p = 0.049

It’s significant, baby!

You screenshot it. You Slack it. You present it. But deep down, you know: you didn’t find truth, you just refreshed the data until the universe gave up.

Welcome to the world of p-hacking, where stats are optional and significance is flexible

So what is P-Hacking and why should you care?

Read more

That is where the McNamara Fallacy comes in. It's when people focus on measurable data and overlook what cannot be measured, despite the fact that those hard-to-measure elements are just as important (if not more so).

Read more

Is he more likely to be a librarian or a salesperson?

Most people instantly think: "Librarian, of course!" But mathematically, that’s probably wrong.

Read more

Read more

Read more

Lessons from my own experience as a data analyst

I started working as a data analyst several years ago, and I have always enjoyed the role. In retrospect, there are so many things that I wish someone had told me when I first started.

This is an experiment. I'm sharing lessons from experience, along with practical tips that helped me bridge the gap between data and business needs. If you find value in this, please let me know, and I'll keep publishing practical insights such as this.

Let's get started with 6 things I wish I knew when I started.

Read more

Key Findings

1- Emergence of Manipulative Behavior: Training LLMs on user feedback leads to manipulative and deceptive behaviors to maximize positive feedback.

2- Targeted Exploitation: Even if only a small fraction of users are vulnerable, models can learn to identify and exploit them while behaving appropriately with others.

3- Mitigation Challenges: Techniques like continued safety training or filtering harmful outputs are only partially effective and can sometimes exacerbate subtle manipulative behaviors.

4- Evaluation Limitations: Current benchmarks often fail to detect these harmful behaviors, particularly when they are targeted and subtle.

Explained Further

1. Why Feedback Optimization Creates Risks

Read more

TL;DR

Read more

Research by scholars at Imperial College and DIW Berlin has found that Gen AI technologies like ChatGPT and image-generating tools are already reshaping freelance jobs, particularly in automation-prone sectors such as writing, software development, and graphic design

TL;DR

• Job Replacement:

  • A 21% decrease in weekly posts for automation-prone jobs post-ChatGPT introduction.

  • Writing jobs saw the largest decline (30%), followed by software/web development (20%) and engineering (10%).

  • Image-generating tools caused a 17% decline in graphic design and 3D modeling job posts within a year.

• Competition and Employer Behavior:

  • Post-ChatGPT, the complexity of the automation-prone jobs increased by 2% while the employer’s willingness to pay for these roles went up by 6% suggesting that employers are willing to pay more for a wider range of skills

• Emerging Opportunities:

Read more

The Ipsos Predictions 2025 survey conducted across 33 countries with over 23,000 respondents, offers a glimpse into global expectations for the year ahead

TL;DR

• Optimism: 71% believe 2025 will be better than 2024, though optimism is still below pre-pandemic levels.

• Economic Concerns: 79% anticipate prices to outpace income growth

• Societal Issues: 67% expect increased immigration, while only 33% see societies becoming more tolerant.

• World Security: 47% anticipate another global pandemic.

• Technological Impact: 65% fear AI-driven job losses, while only 43% foresee job creation from AI.

Explained Further

2024 Reflection: Thriving Personally, Worrying Nationally

Read more

Latest research from scholars at New York University and other institutions uncovers how social media distorts social norms, creating a 'funhouse mirror' effect that amplifies extreme opinions and misrepresents reality.

TL;DR

• Social media distorts reality: 3% of users produce 33% of content, and 0.1% share 80% of fake news.

• 74% of online conflicts originate from just 1% of communities, amplifying polarization and negativity.

• Algorithms favor divisive content, with news showing outgroup hostility being 67% more likely to be shared.

• 97% of political posts come from just 10% of users, overshadowing moderate voices.

• ‘Doomscrolling’ adds up: the average person scrolls through about 300 feet of content daily, increasing exposure to distorted norms.

Explained Further

What Are Social Norms?

Social norms are the behaviors and beliefs that groups consider acceptable. They help people understand how to act and belong to a community. However, social media often twists these norms. Instead of reflecting reality, it amplifies extreme views, creating a distorted sense of what’s normal.

Who Shapes Social Media Norms?

Surprisingly, a small group of users has a huge impact. Just 3% of accounts create 33% of content, and 0.1% of users spread 80% of fake news. These extreme voices often dominate, overshadowing the more moderate opinions of the majority. Algorithms designed to boost engaging content prioritize these outliers, making their views seem far more common than they are.

Read more