Connect with us

Misc

Here are 15 Common Data Fallacies to Avoid

Published

on

In today’s tech-driven economy, data is essential for gaining new insights, making decisions, and building products.

In fact, there is so much data out there, that the quantity of it is doubling every two years⁠—and by 2025, there will be 175,000 exabytes of data in existence.

This is an unprecedented figure, and it’s hard to put into perspective. To give you some sense, a single exabyte is equal to 1,000,000,000 GB of data, and five exabytes has been said to be roughly equal to “all of the words ever spoken by mankind”.

Common Fallacies With Data

As you can imagine, digging through all of this data can be quite the challenge.

Data comes in many different forms and not all of them are easy to analyze. As a result, it is tempting to take shortcuts with data, or to try and fit the incoming data we receive into our pre-conceived notions of how things ought to be.

Today’s infographic comes to us from Geckoboard and it shows the common mistakes that people make in analyzing data. We’ve reformatted their PDF to fit here.

15 Common Data Fallacies

Here are 15 Common Data Fallacies to Avoid

How do we avoid painting a bullseye around the arrow, so that we can interpret the meaning of data in a logical, consistent, and methodological way?

The key is to understand common mistakes that people make with data, and why these errors skew our interpretations.

Examples of Fallacies

Here are four examples of fallacies, and why each is considered a faux-pas by data scientists.

1. Survivorship Bias

When people analyze the qualities it takes to be a successful entrepreneur, we typically look at the existing population of established entrepreneurs for clues. However, by limiting our sample just to this “surviving” group of entrepreneurs, we run the risk of survivorship bias.

There are lessons we can learn from all of the entrepreneurs who have failed—they are just much harder to find. Integrating that data into the story can help complete a much fuller picture.

2. False Causality

Did you know that there is a 95% correlation between the marriage rate in Kentucky and the amount of people who drown each year from falling out of fishing boats? (See it, and other bizarre correlations here)

Kentucky marriages vs. people who drown

Does this mean that there is some sort of relationship between the two variables?

Finding a high level of correlation can happen simply by chance—but awarding false causality is one of the most amateur statistical mistakes in the book.

3. The Gambler’s Fallacy

If the roulette wheel turns up black for 26 times in a row, does that mean that it will revert back to red on the next spin?

It’s easy to say that the odds don’t change, but imagine being in the moment. The Gambler’s Fallacy happens with data analysis as well: just because something happens unusually frequently over a period of time doesn’t mean that nature will “even it out”.

4. The Cobra Effect

Data can be used to measure progress in achieving business goals, but what if there is incentive to game these goals?

Wells Fargo, in an effort to upsell existing clients, introduced an incentive called “eight is great”. In short, their employees were encouraged to sell eight accounts per customer, which could take the form of credit cards, savings accounts, and other financial services.

In an example of good intentions gone awry, Wells Fargo employees began breaking the rules to meet their targets. Millions of unauthorized credit card and deposit accounts were opened based on this perverse incentive, and the bank was eventually ordered to pay a $142 million settlement.

Click for Comments

Demographics

Mapped: Population Growth by Region (1900-2050F)

In this visualization, we map the populations of major regions at three different points in time: 1900, 2000, and 2050 (forecasted).

Published

on

Map of Population Growth by Region

Mapping Population Growth by Region

This was originally posted on our Voronoi app. Download the app for free on iOS or Android and discover incredible data-driven charts from a variety of trusted sources.

In fewer than 50 years, the world population has doubled in size, jumping from 4 to 8 billion.

In this visualization, we map the populations of major regions at three different points in time: 1900, 2000, and 2050 (forecasted). Figures come from Our World in Data as of March 2023, using the United Nations medium-fertility scenario.

 

 

Population by Continent (1900-2050F)

Asia was the biggest driver of global population growth over the course of the 20th century. In fact, the continent’s population grew by 2.8 billion people from 1900 to 2000, compared to just 680 million from the second on our list, Africa.

Region190020002050F
Asia931,021,4183,735,089,7755,291,555,919
Africa138,752,199818,952,3742,485,135,689
Europe406,610,221727,917,165704,398,730
North America104,231,973486,364,446679,488,449
South America41,330,704349,634,344491,078,697
Oceania5,936,61531,223,13357,834,753
World 🌐1,627,883,1306,149,181,2379,709,492,237

China was the main source of Asia’s population expansion, though its population growth has slowed in recent years. That’s why in 2023, India surpassed China to become the world’s most populous country.

Southeast Asian countries like the Philippines and Indonesia have also been big drivers of Asia’s population boom to this point.

The Future: Africa to Hit 2.5 Billion by 2050

Under the UN’s medium-fertility scenario (all countries converge at a birthrate of 1.85 children per woman by 2050), Africa will solidify its place as the world’s second most populous region.

Three countries—Nigeria, Ethiopia, and Egypt—will account for roughly 30% of that 2.5 billion population figure.

Meanwhile, both North America and South America are expected to see a slowdown in population growth, while Europe is the only region that will shrink by 2050.

A century ago, Europe’s population was close to 30% of the world total. Today, that figure stands at less than 10%.

Continue Reading

Subscribe

Popular