My Everyday Carry: Statistical Tools

There’s a trend on youtube with some folks showing their “EDC” or what they call “Every day carry.” It’s the stuff that is quite practical and, at times, a bit of a ‘flex’ on their personal stuff.  The obvious things that are rational are: new wallets with RFID protections, a small notebook with a side pen, nice keychains with small pen lights, and other practical stuff in urban settings. Then there are the ‘flex’ items: concealed handguns, highpowered portable torchlights (handy when trapped in the underground tunnels!), solar powered watches, maybe some deadly weapons like a light brass knuckle or butterfly knives even. I personally like the idea of sharing your personal EDCs, because they say so much about a person, and their interests. 

Following this trend, I would like to share my EDCs - but they are not ‘things’ nor do I consider them any ‘flex’ at all. Instead, they are mathematical / statistical concepts that help me improve my decision making. Advantages? They are light, virtual, carried only in my brain, and do not occupy too much physical space at all. And, they are easily accessible if I need them … and I do pull them out often! 

My EDC things are: “the statistical trio,” “probabilities”, and “forecasting.” Each tool is handy as I navigate the world and make decisions based on my these tools. I hope these will help you as well. Let’s start with the first tool. 

The Statistical Trio is based on the math concepts of statistical curves. I am sure you have seen the “bell curve.” Within the curve there is a concept called the ‘Center’ - where it describes 3 important points in the curve: the “mean”, “median” and “mode.”

Lets start with what everyone knows as the “average” - also called the “mean.” The mean is calculated by adding up all the values in a dataset (a.k.a. data points) and dividing by the total number of values. It provides a sense of the "typical" or average value. 

Application: If you take the bus on a regular basis, you will probably know three specific exact times the bus arrives at the pickup point. The average time will help you understand what time you should exactly be there in advance, to ensure that you do catch it before it leaves.

The other last of the Statistical Trio, is called the “median.” Imagine you have to cut a chocolate bar evenly. How do you approach this? The “median” is the middle value when data is arranged in order. It's less influenced by extreme values (at the left and right of a curve, often called the “outliers”) compared to the mean. When you need to cut that chocolate bar evenly to share with your sibling, you usually aim for this middle so you both get exactly the same amount of chocolate. That’s the median. This helps you ‘eyeball’ the middle ground, and come to a just and hopefully ‘fair’ conclusion. It is not always perfect or accurate, but an approximation when you are asked to do a quick decision. Another way to use the median is to take out the “outliers” in a set of numbers and focus only on the center.  This results in a decision that is not ‘pulled’ or influenced by the extreme numbers. Example - Knowing the median salary for a role can help you make a more realistic salary request, as it's less affected by a few very high executive salaries.

And then there’s the “mode.” The mode is the most frequently occurring number in a dataset. There can be one mode, more than one mode (bimodal), or no mode at all. This becomes more important when one needs to use either the “average” or the most common number / “mode” when creating a fair formula.  

Application: For example, let's say we have the following set of monthly incomes: 5000; 7000; 8000; 5000; and 10,000. You want to use this data set to estimate a percentage of income to charge rent. The analysis indicates that the mode is a better tool to use.

The *mean* or “average” income is (5000 + 7000 + 8000 + 5000 + 10000) / 5 = $7,000. So a person could say that the average income is $7,000 for this set of data. Only one person earns that, and two others earn more than that. But two people will not be able to pay the charge since they earn below the average.

The *mode* is $5,000, as it appears twice. The mode refers to the number that most frequently shows up in that set. Therefore it is worth using this rather than the average of $7,000. Why? Because It is a lot more realistic since more people earn only that much, and not $7,000. The benefit - everyone benefits from a more realistic housing charge. 

The second EDC for me is “Probabilities.” Probability is the mathematical study of chance and uncertainty. While it might sound complex, probability is a part of our everyday lives. From weather forecasts to lottery odds, probability helps us understand the likelihood of different “outcomes.” The outcome of a random experiment is called an event. For instance, when flipping a coin, the possible events are heads or tails. 

Probability is expressed as a number between 0 and 1. A probability of 0 means an event is impossible, while a probability of 1 means it's certain to happen. For example, the probability of getting heads when flipping a fair coin is 1/2 or 0.5, or 50%.

Application: When you check your phone’s weather app, and want to know what is the probability of rain today, the app shows it in terms of percentage. You can then make a decision based on this chance. 

Finally, the third EDC is Forecasting. Statistics is not confined to theoretical calculations; it's a practical tool used across various events across and various instances. One of its most powerful applications is forecasting. By analyzing historical data and identifying patterns, we can make smarter predictions about things in the future.

There are many kinds of methods of forecasting. A common one I use a lot is linear regression. This technique involves creating a “line” on a graph that best "fits" the historical data points ... that you can thus use to predict the next price point. I use this a lot when I estimate future prices of goods. Imagine you collected over time the price of chicken. By using either the average (see above, “mean”) of all the prices, you arrive at a price that you think is going to be labeled when you go buy chicken the next time.  You could also use the “mode” or the most common price you see on that data set as your predicted price.

Forecasting has many applications, from price determination, to sports analysis, to inventory projections, and to stock market purchases.  This tool can be simple math or could be sophisticated depending on the level of complexity you want to apply. One example is forecasting the time your friend arrives at the bar to meet you. Based on his past results, he is usually 15 minutes later than the agreed time.  This means, you have to forecast that he will be consistently late - by about 15 minutes.  You can give a +/- 5 minutes as an estimation error. Knowing this, you won't stress about it and know your friend will be on the way given his nature.  

And there you have it, the three EDC Tools that I use everyday.  I pull these things out when I need a moment before I make a decision.  What about you? What tools do you carry everyday mentally that help you make choices? Remember, you have the Agency to make decisions with tools to help you aim for the best outcomes.