A key component of making good decisions, financial or otherwise, is learning to think critically. This is especially important when so many people are bombarding you with misleading, flawed logic for their own gain (arguably this is the main role of the advertising industry). You need to be able to recognize these traps and navigate around them, my short list is below:

1. “If A then B” does not necessarily mean “If B then A”
2. Correlation is not Causation
3. A data point is not necessarily representative of a larger trend
4. The primary motivation for every for-profit company, is profit
5. Gambling is hoping for improbable outcomes, decisions should be based on most probable outcomes
6. Data and Information are not the same

We’ll look at these one by one.

1. “A then B” does not mean “B then A”. This is a concept taught in your middle school math class, but how many have forgotten middle school math (or middle school entirely–thank goodness)? This is easiest to illustrate with an example. If I say “Millionaires invest in stocks”, that does not mean “If I invest in stocks, I’ll be a millionaire”. Confused? Look at it from a Venn diagram perspective (by the way, Venn diagrams are on the Mount Rushmore of diagrams. I know this doesn’t exist, but it should)

In this case, the statement says that investing in stocks is necessary to be a millionaire, but not a guarantee of being a millionaire. FYI, this is just an example, plenty of successful investors invest exclusively in real estate, for example. I see this concept constantly confused, in politics, science discussions, health care debates, and many other arguments. Try to keep this in mind.

2. Related to the above is the causation/correlation debate. In short, correlation is simply showing two things are related (when you see one, you often see the other), without making a statement about if one thing caused the other. This is much easier to show than causation, which requires careful study and accounting for all variables. You can see where this is particularly difficult in the field of nutrition, where there are so many different variables among people’s behavior. For example, what if I say “People who eat fast food have a much higher incidence of poor health”, or in Venn diagram mode:

Again, just a made up example, though I’d imagine the actual data are similar. The above says poor health and fast food are correlated but can we say fast food causes poor health? The above information is not enough to make that statement, because I haven’t shown you how we’ve accounted for other variables. For example, maybe we note that many people who eat fast food are financially disadvantaged (fast food is cheap) and we find later it is their financial position that is the true cause of their poor health. Or, maybe they eat fast food as comfort because of their poor health (the poor health causes fast food!). Now it may very well be that the fast food is in fact causing the poor health, but before we start making cause statements we need to make sure we have done specific study for other potential influences. You can see why this is so important, and why proper science is needed behind anyone’s claims. We often find ourselves in a situation wanting a certain outcome (health, wealth) but can only try to create the outcome indirectly (eat specific foods, make specific investments), so causation is critical. Otherwise, we may end up wasting time, money, and other resources chasing ineffective influences for the outcomes we want. Again, this is constantly confused. Watch any prime time news show and wait for someone to confuse these concepts, you won’t need to wait long.

3. The data point as evidence is constantly used in various advertising campaigns, and is particularly popular with financial seminars/software tools.

“Here’s our great financial system/strategy. It’s great! Meet Joe (close up on Joe): ‘I’ve used the XYZ System for 2 years and crushed the market!’. And Sue says ‘I’ve outperformed the market too!'”

So, many people look at this and figure if Joe and Sue could do it, so could they. But what information do we really have about the XYZ System? We have the following, graphically,

Joe and Sue weren’t lying, they did beat the market, but this really isn’t evidence of the utility of the XYZ System, what we really need is the data from all the users of the XYZ System, and my guess is the results would look more like this:

If we had the full picture, we could see that Joe and Sue’s results weren’t representative of the whole, but actually outliers. In many ads they effectively admit this by putting in the fine print “Results not typical”. If the above chart were the truth, would you still pick XYZ System to beat the market? Of course not. This is why ad companies love testimonials. They can be truthful, but cherry pick the best results, which could be random chance, and conveniently ignore data points that don’t make the results look good. Truthful, but deceptive, which is the essence of advertising.

4. Another common ploy of people selling stuff is they try to hide the fact that their motivation is to make as much money as possible, specifically get your money. This is common in infomercials where a person selling a get rich quick scheme will talk about how they figured it out, got rich, and now all they want to do is help people do the same. No, they don’t. They want to get richer, there are plenty of other ways they could try to save the world if that was their goal. Bill Gates got rich and wants to save the world, do you see him doing infomercials for software? No, he’s busy trying to eradicate disease in Africa.

Remember, corporations aren’t here to help you, be your friend, give you a job, or make the world a better place. If their goal were to help people, they’d sell their product at cost for no profit, thereby getting to the max people possible. The product, employing people, and hopefully moving mankind forward are all tools for corporations to ultimately make money. This isn’t good or bad, just a reality that needs to be understood. This doesn’t mean corporations don’t care about their employees or their greater impact on society, or have other altruistic intentions. However always remember their primary reason for existence is figuring out how to get as much of your money as possible.

5. I’ve written on this before, decision making is based on probabilities, rarely guarantees. This means you occasionally will get a bad result despite making a good decision (and good results from bad decisions). The result is not necessarily always a judge of a decision. Good decision making involves taking in all available information, using prior experience and sound logic, and choosing a course of action based on the most probable outcome, e.g.

• You expect to do well if you invest regularly in a diversified portfolio
• You expect to keep your job if you work hard
• You expect to stay healthy if you eat right and exercise

All of the above are probable but not guaranteed. This is why cash in an emergency fund for unexpected life events (e.g. job loss) is important and insurance against catastrophic loss (life, health, home, auto) is important. Knowing bad things can happen despite your best decision process is the rational for protecting yourself. It’s a small financial penalty for large peace of mind.

6. Data is not information. This is also a classic slight of hand in financial commercials. Look at all these charts and graphs we have! Let’s show you a wise, semi-grey-haired man knowingly (maybe even smugly) hitting enter on his computer, leaning back in his chair with his hands on his head with a smirk on his face, knowing he has charts and graphs. Charts and graphs are definitely data. They might even use proprietary algorithms. (Aside, “proprietary” also does not mean “useful”. Proprietary just means they have established that others can’t use the same approach without their permission. One would presume they wouldn’t do this unless the approach was actually useful, but that is not necessarily the case). The key is what they are giving you is not information unless it’s useful. I could develop an algorithm to buy or sell oil based on Alaskan temperature, but unless that algorithm is consistently predictive of the direction of the price of oil, this is just noise, not information. Financial companies imply their data is useful, but generally don’t outright claim this because likely they can’t. Useful information would mean using these data would allow the user, on average, to consistently beat the market, and study after study shows this is not achievable by almost anyone in the financial industry.

I’m sure I’ll think of more to add to this list, but if you keep the above in mind when listening to a Congressman come into Congress with a snowball as evidence that Climate Change isn’t happening…Wait a minute sir, that’s just a data point…not evidence of a larger trend…can I see the evidence of the larger trend?…Oh. Damn.