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 2020, there will be 45,000 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 data into our pre-conceived notions of how things ought to be.
15 Common Data Fallacies
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 in-depth examples of fallacies, and why each is considered a faux-pas by data scientists.
When people analyze the qualities it takes to be a good entrepreneur, we typically look at the existing population of successful entrepreneurs for clues. However, by limiting our sample just to this “surviving” group of entrepreneurs, we run the risk of survivorship bias.
There are certainly 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.
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, an other bizarre correlations here)
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.
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?
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”.
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.
Interactive Map: Tracking Global Hunger and Food Insecurity
Every day, hunger affects more than 700 million people. This live map from the UN highlights where hunger is hitting hardest around the world.
Interactive Map: Tracking Global Hunger and Food Insecurity
Hunger is still one the biggest—and most solvable—problems in the world.
Every day, more than 700 million people (8.8% of the world’s population) go to bed on an empty stomach, according to the UN World Food Programme (WFP).
The WFP’s HungerMap LIVE displayed here tracks core indicators of acute hunger like household food consumption, livelihoods, child nutritional status, mortality, and access to clean water in order to rank countries.
After sitting closer to 600 million from 2014 to 2019, the number of people in the world affected by hunger increased during the COVID-19 pandemic.
In 2020, 155 million people (2% of the world’s population) experienced acute hunger, requiring urgent assistance.
The Fight to Feed the World
The problem of global hunger isn’t new, and attempts to solve it have making headlines for decades.
On July 13, 1985, at Wembley Stadium in London, Prince Charles and Princess Diana officially opened Live Aid, a worldwide rock concert organized to raise money for the relief of famine-stricken Africans.
The event was followed by similar concerts at other arenas around the world, globally linked by satellite to more than a billion viewers in 110 nations, raising more than $125 million ($309 million in today’s dollars) in famine relief for Africa.
But 35+ years later, the continent still struggles. According to the UN, from 12 countries with the highest prevalence of insufficient food consumption in the world, nine are in Africa.
|Country||% Population Affected by Hunger||Population (millions)||Region|
|Burkina Faso 🇧🇫||61%||19.8||Africa|
|South Sudan 🇸🇸||60%||11.0||Africa|
|Sierra Leone 🇸🇱||55%||8.2||Africa|
|Syria 🇸🇾||55%||18.0||Middle East|
|Yemen 🇾🇪||44%||30.0||Middle East|
Approximately 30 million people in Africa face the effects of severe food insecurity, including malnutrition, starvation, and poverty.
Although many of the reasons for the food crisis around the globe involve conflicts or environmental challenges, one of the big contributors is food waste.
According to the United Nations, one-third of food produced for human consumption is lost or wasted globally. This amounts to about 1.3 billion tons of wasted food per year, worth approximately $1 trillion.
All the food produced but never eaten would be sufficient to feed two billion people. That’s more than twice the number of undernourished people across the globe. Consumers in rich countries waste almost as much food as the entire net food production of sub-Saharan Africa each year.
Solving Global Hunger
While many people may not be “hungry” in the sense that they are suffering physical discomfort, they may still be food insecure, lacking regular access to enough safe and nutritious food for normal growth and development.
Estimates of how much money it would take to end world hunger range from $7 billion to $265 billion per year.
But to tackle the problem, investments must be utilized in the right places. Specialists say that governments and organizations need to provide food and humanitarian relief to the most at-risk regions, increase agricultural productivity, and invest in more efficient supply chains.
Mapped: Distribution of Global GDP by Region
Where does the world’s economic activity take place? This cartogram shows the $94 trillion global economy divided into 1,000 hexagons.
Mapped: The Distribution of Global GDP by Region
Gross domestic product (GDP) measures the value of goods and services that an economy produces in a given year, but in a global context, it is typically shown using country-level data.
As a result, we don’t often get to see the nuances of the global economy, such as how much specific regions and metro areas contribute to global GDP.
In these cartograms, global GDP has been normalized to a base number of 1,000 in order to show a more regional breakdown of economic activity. Created by Reddit user /BerryBlue_Blueberry, the two maps show the distribution in different ways: by nominal GDP and by GDP adjusted for purchasing power parity (PPP).
Before diving in, let us give you some context on how these maps were designed. Each hexagon on the two maps represents 0.1% of the world’s overall GDP.
The number below each region, country or metropolitan area represents the number of hexagons covered by that entity. So in the nominal GDP map, the state of New York represents 20 hexagons (i.e. 2.0% of global GDP), while Munich’s metro area is 3 hexagons (0.3%).
Countries are further broken down based on size. Countries that make up more than 0.95% of global GDP are broken down into subdivisions, while countries that are smaller than 0.1% of GDP are grouped together. Metro areas that account for over 0.25% of global GDP are featured.
Finally, it should be noted that to account for some outdated subdivision participation data, the map creator calculated 2021 estimates for this using the formula: national GDP (2021) x % of subdivision participation (2017-2020).
Nominal vs. PPP
The above map is using nominal data, while the below map accounts for differences in purchasing power (PPP).
Adjusting for PPP takes into account the relative value of currencies and purchasing power in countries around the world. For example, $100 (or its exchange equivalent in Indian rupees) is generally going to be able to buy more in India than it is in the United States.
This is because goods and services are cheaper in India, meaning you can actually purchase more there for the same amount of money.
Anomalies in Global GDP Distribution
Breaking down global GDP distribution into cartograms highlights some interesting anomalies worth considering:
- North America, Europe, and East Asia, with a combined GDP of nearly $75 trillion, make up 80% of the world’s GDP in nominal terms.
- The U.S. State of California accounts for 3.7% of the world’s GDP by itself, which ranks higher than the United Kingdom’s total contribution of 3.3%.
- Canada as a country accounts for 2% of the world’s GDP, which is comparable to the GDP contribution of the Greater Tokyo Area at 2.2%.
- With a GDP of $3 trillion, India’s contribution overshadows the GDP of the whole African continent ($2.6 trillion).
- This visualization highlights the economic might of cities better than a conventional map. One standout example of this is in Ontario, Canada. The Greater Toronto Area completely eclipses the economy of the rest of the province.
Inequality of GDP Distribution
The fact that certain countries generate most of the world’s economic output is reflected in the above cartograms, which resize countries or regions accordingly.
Compared to wealthier nations, emerging economies still account for just a tiny sliver of the pie.
India, for example, accounts for 3.2% of global GDP in nominal terms, even though it contains 17.8% of the world’s population.
That’s why on the nominal map, India is about the same size as France, the United Kingdom, or Japan’s two largest metro areas (Tokyo and Osaka-Kobe)—but of course, these wealthier places have a far higher GDP per capita.
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