99% of people will lose over a half of a million dollars a decade on just this one mistake. Think about that cost. That exceeds the cost of a cool Tesla Roadster. That's more than two McMansions in the United States in most places. That's more money to travel the world than most people will spend their entire life!
Most people will think that they are the exception to this as well, even though they aren't. You're probably no different.
What is this mistake?
Over a decade ago, a friend of mine used collected data to conclude something that cost her thousands of hours per year. If we assume that she made the median individual income of $35 an hour, she was losing over $35,000 a year. After a decade, she had lost over a million dollars, when you consider how her time and money could have been used for other productive means. Wrong assumptions cost. Most people will never know how much their poor assumptions cost.
It's always fun to review statistics! Some great videos where we dig into the basics of statistics.
- Why Precisely Defining the Population Matters
- Why Ceteris Paribus Matters In Statistics
- What Is the Average (Mean) and When To Use It
- What Is Regression To the Mean and Why Does It Matter?
- What Is Median? Why Use Median?
- What Is the Mode?
- What Is the Law of Large Numbers?
- Applied Example of What We've Learned So Far
- How To Calculate the Variance and the Standard Deviation
- How To Calculate the Standard Deviation Part II
- TSQL: How To Get the Percentile
- TSQL: How To Get the Quartile
- TSQL: How To Get the Outliers In A Data Set
- Data Tendency and Spread: Practice Examples
- Data and Variable Types
- How We'll Use Mathematics and Statistics In This Series
- What Is Probability?
- Considerations With Random Probability
- Factors Influencing Probability
- Probability of Independent Random Events
- TSQL: Gambler's Fallacy Demo (Surprise Too)
- Gambler's Fallacy Part 2 (Follow-Up)
- Conditional Probability
By contrast, I saw an almost mirrored situation with another friend. But made one decision that differed. Before executing with poor data, he consulted with others about alternative views. This didn't mean he wanted to hear these views, but he considered their truth. Following the counsel, he stopped wasting hours of his life on the wrong data he had.
I say often that no data will always be better than bad data. In addition, looking at alternative views is a great way to ensure that you're not paying for bad data. Nonetheless, 99% of people will never do this. People inherently do not want to save time or money getting accountability.
Check out the highest-rated Automating ETL course on Udemy, if you're interested in data. From some of the reviews:
Many "data promises" from various industries will fail (and have failed). About a decade ago, one American data scientist promised me that within a few years we'd have the cure for cancer, as data was making things possible in health care that were never possible before that time. Those years have passed and no cure for cancer has been found. In addition, the United States has now seen three consecutive years of declining life expectancies. Why did someone with so much data fail to see the future? Why were her predictions incorrect? What other findings did she have?
This post was sponsored by Maixin Research - a firm that specializes in contrarian research. They are featuring a contrarian in-focus opportunity for 2019 for private clients worldwide. Image from Pixabay.