On Making Good Predictions – Part 1


Let’s be honest−we love making predictions and forecasting the future.

Sports, economy, stocks, politics, technology, you name it−we have an opinion on every important topic and every important upcoming event.  My guess is that we feel that this truly shows our intelligence and shows our knowledge, as well as our ability to process all of the available information into a few simple conclusions.  Plus, it makes us more interesting as conversationalists, more popular, and more desirable.

I think that when we do it, we honestly believe in what we are saying, and that we are actually pretty good at predicting the future.  Otherwise, we would be more careful and hedge our predictions more frequently.

Often, to make our point stronger, we refer to various subject matter experts and quote them with confidence:  after all, they are the pros, the masters of their domain. They definitely know what they are talking about!  Doing this probably enhances our own image and turns us into something between an actual subject expert and a highly-informed high-IQ hobbyist.

But one thing is guessing the future for fun during a friendly chat.  Another thing is turning this into your profession and influencing millions of people while getting paid for it.  And the world around us is filled with those who make business out of making predictions while contributing little value.

The wilder a prediction is the better. Predictions and forecasts are everywhere: in books, on television, on the radio, and even in big lecture halls, telling us what is bound to happen next. Gold will go down (or up).  Oil will go to $200 (or to $20).  Market will gain (or decline) before the end of this year.  Or next year.  Economic recession is coming.  And the singularity is near.

For some reason, we live in a world that encourages outlandish statements and predictions and doesn’t really punish those who are wrong (even publicly & repeatedly wrong), thus skewing the Risk-Benefit ratio in the favor of risk-taking.  The people who make bold predictions are called visionaries, futurists, and thought leaders even if they are wrong a lot (or even all) of the time. So they often end up turning this prediction-making into highly profitable businesses.

Here’s a recent example:  Nate Silver and his FiveThirtyEight company. After Nate Silver successfully called the outcomes in 49 of the 50 states in the 2008 U.S. Presidential election, he was named one of The World’s 100 Most Influential People by Time (link)!  In the 2012 United States presidential election, Silver correctly predicted the winner of all 50 states and the District of Columbia (link).  But, in 2016, his predictions happened to be the opposite of the reality, as could be seen in one of the pre-election forecasts below.


However, I am pretty sure that this “little” mistake will have little impact on the popularity of FiveThirtyEight services and their business.  Some explanations will be provided (“polls were skewed,” “people were hiding their true intentions,” or something like it), excuses made, and – by next elections or sooner – it will be all forgotten.  Which demonstrates my exact point:  taking risks in making bold predictions pays off.

Now, imagine making some wrong predictions about where to find food or water or where an armed enemy is hiding− imagine making mistakes like these thousands of years ago?  A wrong prediction then could mean death or extreme misery to the prophet himself, to the entire tribe, or to the whole army that trusted him.

The Battle of the Teutoburg Forest

The Battle of the Teutoburg Forest took place in 9 CE (Common Era), in which an alliance of Germanic tribes ambushed and decisively destroyed three Roman legions.

Make a wrong prediction once or twice, and nobody would trust you any time soon.  In fact, you would most likely not even survive your first wrong prediction.  That environment probably discouraged taking risks unless all other “cautious” options were attempted.

To be continued…

This entry was posted in Analytics, data analytics, big data, big data analytics, data on the internet, data analytics meaning, Data Analysis and Visualization, Past, present, and future and tagged , , . Bookmark the permalink.

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

This site uses Akismet to reduce spam. Learn how your comment data is processed.