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_{Exploring data collection, selection bias, and everyday vs occasional}

Happy Thursday! Thanks for reading Intentional Dollar — where we look at old money ideas through a new perspective.

What’s inside?

• One idea to experiment with

• Two quotes from others

• Three questions to dig deeper

• Four lines of poetry for the point

Disclaimer: This is not investment advice. These weekly posts represent my simple thoughts, a few quotes, and some questions — for educational purposes only.

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_{One idea to experiment with:}

Data Collection:

Without measurement, we have no real indication of where a thing stands.
How much do you weigh? You have to measure to know.
How much are you worth? You have to measure to know.

In isolation, measurement is wobbly. But when stretched across time—each point connected by others—it creates a valuable dataset. Measuring across time is still not enough though. We must ensure that what we measure matters and we need to ensure we are collecting good data.

Data is born from measurement, and measurement is a fertile fountain: the more you measure, the more data you create. But the real value comes after measurement. Measuring and producing data in isolation is meaningless. It’s the next step—analysis—that gives data meaning.

Let’s drill into the weight example, assuming the goal is to lose weight. When the process begins—whether you're aware of it or not—you’re collecting data. Even if you never step on a scale, you’re still observing: the mirror check, the calorie count, the compliment from your spouse. These are all mental data points.

From there, we analyze: What I’m doing is/isn’t working. I need to work out more. I need to eat healthier, etc.

Let’s say you’re disciplined enough to weigh yourself every day. Great—you now have consistent data. You can see trends and progress.

But there’s a critical gap: have you questioned the quality of your data?

If the scale goes up, you know something’s off in your process. But even with diligently collected data, you may not know why. And if you don’t know why, your ability to make a change is compromised.

We are only as good as our data. If I collect bad data and make a good recommendation, the recommendation is bad. The fault wasn’t in the analysis—it was in the data.

Bad inputs, bad outputs.

So, our job is to be obsessive collectors of good data. And good data comes from good measurement. Only then can meaningful analysis begin.

Back to weight loss.

Let’s say the scale has gone up for three days. You recall drinking red wine at the work dinner on Wednesday. You also had dessert—because it was free. On Thursday, you felt tired from poor sleep and ordered takeout because you didn’t have the energy to cook. Friday, you picked up pizza for the family to celebrate the end of the week.

With this in mind, you chalk it up to short-term slip-ups and move on, recommitted to the goal.

But how insightful was your data?

What if you measured more?

Suppose you write down your meals or types of food—not calories, just general entries. This simple habit adds another layer of data to your scale number.

Now you see that every Friday night, you order pizza. Every month, there’s a company dinner. Every time you splurge, the following day includes takeout. What once looked like isolated events now reveals a pattern.

With better data, you gain better insight. You move from generic mental chatter (work harder, be better, be more disciplined) to specific strategies. Now you can actually solve the problem.

Let’s extrapolate this out:

• If you don’t know where your time goes, measure it. Check screen time. Bucket the problem areas: social media? Email? Texting? Remove slot-machine apps. Turn off non-emergency notifications.

• If you don’t know where your money goes, measure it. Tracking spending gives you data. Tracking why you spent gives you better data.

Specific solutions come from thoughtful analysis. Thoughtful analysis comes from robust data. And robust data comes from careful measurement—not thin air.

_{Two quotes on selection bias:}

We naturally see a misrepresentation of the world when we take information in. We see the news as the truth, as a factual spring to take our daily knowledge. But there are biases in selection, from word to image, from story to headline. What we include, what we leave out shapes the conveyed message.  

_{Three questions on everyday vs occasional:}

1. What facets of life am I an everyday person and an occasional person?

2. What things should I do daily yet I only do them when I feel like it? What about daily actions that only need to be done occasionally?

3. What am I missing by waiting for the right feelings to emerge?

Which question stuck with you? Questions like these are spotlights for the mind. Reply to this email and let me know which one shined light on a previously dark cave.

_{Four lines of poetry for the point:}

Contact Me:

Content ideas, questions? Reply to this email or reach out to me at [email protected]

Disclaimer: This is not investment advice. These weekly posts represent my simple thoughts, a few quotes, and some questions — for educational purposes only.

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