Business Without the Bullsh*t: 49 Secrets and Shortcuts You Need to Know (37 page)

But just a moment! How can Jim possibly know if his project will finish on time when he doesn’t yet know the test results? Therefore, Jim’s yes is a half-truth that should signal Jill to press the point further.

Identifying people who tell half-truths allows you to assess whether they can be trusted and whether you can believe their promises and commitments, which allows you to make better decisions.

An indirect lie happens when somebody passes along information that he or she knows is untrue, by claiming he heard that piece of information from somebody else.

For example, Fred knows a layoff is imminent, but when asked about it says, “Jerry doesn’t think so.” Fred may technically be telling the truth (because Jerry is misinformed), but the statement is intended to mislead.

Here’s another example. You can turn almost any false statement into something that’s technically true by preceding it with the phrase “It’s rumored that…” Such statements are always “true” because the statement
starts the rumor
.

To spot an indirect lie, poke at the source that the possible liar is citing. If you find it wanting, ask the question again more directly. For example:

Once again you’ve maneuvered Fred into a position in which he must tell either the truth or a bald-faced lie.

Mark Twain wrote, “There are three kinds of lies: lies, damned lies, and statistics.” Since statistics are traded around the workplace with gusto, it’s in your interest to be able to identify the ones that misrepresent the truth. To do this, ask these five questions:

Statistics are only as valid as the data that lies behind them. As a general rule, if the person or organization that gathers the data will receive some kind of financial benefit if the data is skewed, the data will be skewed.

For example, if a corporation responsible for a large amount of pollution funds a study “proving” that the pollution is harmless, the data in that study is almost guaranteed to be skewed, because otherwise the polluter would be forced to spend money to clean it up.

Companies frequently run Web polls in which people accessing the website decide whether they want to participate in the survey.
However, any statistics based on these “self-selected” polls are automatically bogus.

For example, if I stick on a website a question like, “How well are we doing on customer service?” only people who have had very good or very bad customer service experiences will bother to answer. You’ll have no idea what the typical customer thinks.

Many bogus statistics use averages in a way that’s clearly intended to mislead. For example, in a room with one billionaire and 999 people who are penniless, the average wealth per person is a million dollars. While true, the statistic is misleading.

Rather than averages, valid statistics tend to use the concept of a
median
, which is the middle value when all values are arranged in order. In the example above, the median wealth per person is zero dollars, regardless of the presence of the billionaire.

Even if two sets of data seem to be in lockstep, you have no idea whether that is meaningful until you know for certain that one thing caused the other. Correlation is not causation.

For example, if your sales revenue spikes upward after your salespeople attend a sales training class, the increased revenue
may
be the result of sales training or
may
be the result of something unrelated, like an improvement in economic conditions.

Graphical presentations of data (whether bogus or real) can also mislead. For example, by tweaking the scale of a graph, you can make a small difference look like a big difference (or vice versa):

The graphic gives the impression that there’s been a huge increase in sales, when in fact, sales increased only by a measly .1 percent. Rule of thumb: the fancier the graphs, the more likely it is that the presentation is purposefully misleading.

Another way to distort graphics is to present insignificant amounts in a way that makes them seem significant. For example, if you ask nine customers a question and eight of them answer yes, you create a graphic like this:

However, if you’ve got thousands of customers, it’s impossible that those nine are representative of your entire customer base. You end up with a statistic that seems authoritative but is in fact entirely bogus.