A Field Guide to Lies: Critical Thinking in the Information Age (31 page)

In addition to an anti-science bias, there is an anti-skepticism bias when it comes to the Internet. Many people think, “If I found it online it must be true.” With no central authority charged with the responsibility of monitoring and regulating websites and other material found online, the responsibility for verifying claims falls on each of us. Fortunately, some websites have cropped up that help. Snopes.com and similar sites are dedicated to exposing urban legends and false claims. Companies such as Consumer Reports run
independent laboratories to provide an unbiased assessment of different products, regardless of what their manufacturers claim. Consumer Reports has been around for decades, but it is no great leap to expect that other critical-thinking enterprises will flourish in the twenty-first century. Let’s hope so. But whatever helpful media is out there, each of us will still have to apply our judgment.

The promise of the Internet is that it is a great democratizing force, allowing everyone to express their opinions, and everyone to have immediate access to all the world’s information. Combine these two, as the Internet and social media do, and you have a virtual world of information and misinformation cohabiting side by side, staring back at you like identical twins, one who will help you and the other who will hurt you. Figuring out which one to choose falls upon all of us, and it requires careful thinking and one thing that most of us feel is in short supply: time. Critical thinking is not something you do once with an issue and then drop it. It’s an active and ongoing process. It requires that we all think like Bayesians, updating our knowledge as new information comes in.

Time spent evaluating claims is not just time well spent, it should be considered part of an implicit bargain we’ve all made. Information gathering and research that used to take anywhere from hours to weeks now takes just seconds. We’ve saved incalculable numbers of hours of trips to libraries and far-flung archives, of hunting through thick books for the one passage that will answer our questions. The implicit bargain that we all need to make explicit is that we will use just
some
of that time we saved in information acquisition to perform proper information verification. Just as it’s difficult to trust someone who has lied to you, it’s difficult to trust your own
knowledge if half of it turns out to be counterknowledge. The fact is that right now counterknowledge flourishes on Facebook and on Twitter and on blogs . . . on all the semi-organized platforms.

We’re far better off knowing a moderate number of things with certainty than a large number of things that might not be so. Counterknowledge and misinformation can be costly, in terms of lives and happiness, and in terms of the time spent trying to undo things that didn’t go the way we thought they would. True knowledge simplifies our lives, helping us to make choices that increase our happiness and save time. Following the steps in this
Field Guide
to evaluate the myriad claims we encounter is how we can stay two steps ahead of the millions of lies that are out there on the Web, and ahead of the liars and just plain incompetents who perpetrate them.

APPENDIX
APPLICATION OF BAYES’S RULE

Bayes’s rule can be expressed as follows:

For the current problem, let’s use the notation that G refers to the prior probability that the suspect is guilty (before we know anything about the lab report) and E refers to the evidence of a blood match. We want to know P(G|E). Substituting in the above, we put in G for A and E for B to obtain:

To compute Bayes’s rule and solve for P(G | E), it may be helpful to use a table. The values here are the same as those used in the fourfold table
here
.

Then, rounding, P(Guilty | Evidence ) = .10
P(Innocent | Evidence) = .90

GLOSSARY

This list of definitions is not exhaustive but rather a personal selection driven by my experience in writing this book. Of course, you may wish to apply your own independent thinking here and find some of the definitions deserve to be challenged.

Abduction.
A form of reasoning, made popular by Sherlock Holmes, in which clever guesses are used to generate a theory to account for the facts observed.

Accuracy.
How close a number is to the true quantity being measured. Not to be confused with precision.

Affirming the antecedent.
Same as
modus ponens
(see entry below).

Amalgamating.
Combining observations or scores from two or more groups into a single group. If the groups are similar along an important dimension—homogeneous—this is usually the right thing to do. If they are not, it can lead to distortions of the data.

Average.
This is a summary statistic, meant to characterize a set of observations. “Average” is a nontechnical term, and usually refers to the
mean
but could also refer to the
median
or
mode.

Bimodal distribution.
A set of observations in which two values occur more often than the others. A graph of their frequency versus their values shows two peaks, or humps, in the distribution.

Conditional probability.
The probability of an event occurring
given
that another event occurs or has occurred. For example, the probability that it will rain today
given
that it rained yesterday. The word “given” is represented by a vertical line like this: | .

Contrapositive.
A valid type of deduction of the form:

If A, then B

Not B

Therefore, not A

Converse error.
An invalid form of deductive reasoning of the form:

If A, then B

B

Therefore, A

Correlation.
A statistical measure of the degree to which two variables are related to each other, it can take any value from −1 to 1. A perfect correlation exists (correlation = 1) when one variable changes perfectly with another. A perfect negative correlation exists when one variable changes perfectly opposite the other (correlation = −1). A correlation of 0 exists when two variables are completely unrelated.

A correlation shows only that two (or more) variables are linked, not that one causes the other. Correlation does not imply causation.

A correlation is also useful in that it provides an estimate for how much of the variability in the observations is caused by the two variables being tracked. For example, a correlation of .78 between height and weight indicates that 78 percent of the differences in weight across individuals are linked to differences in height. The statistic doesn’t tell us what the remaining 22 percent of the variability is attributed to—additional experimentation would need to be conducted, but one could imagine other factors such as diet, genetics, exercise, and so on are part of that 22 percent.

Cum hoc, ergo propter hoc
(with this, therefore because of this).
A logical fallacy that arises from thinking that just because two things co-occur, one must have caused the other. Correlation does not imply causation.

Cumulative graph.
A graph in which the quantity being measured, say sales or membership in a political party, is represented by the total to date rather than the number of new observations in a time period. This was illustrated using the cumulative sales for the iPhone [
here
].

Deduction.
A form of reasoning in which one works from general information to a specific prediction.

Double y-axis.
A graphing technique for plotting two sets of observations on the same graph, in which the values for each set are represented on two different axes (typically with different scales). This is only appropriate when the two sets of observations are measuring unlike quantities, as in the graph
here
. Double y-axis graphs can be misleading because the graph maker can adjust the scaling of the axes in order to make a particular point. The example used in the text was a deceptive graph made depicting practices at Planned Parenthood.

Ecological fallacy.
An error in reasoning that occurs when one makes inferences about an individual based on aggregate data (such as a group mean).

Exception fallacy.
An error in reasoning that occurs when one makes inferences about a group based on knowledge of a few exceptional individuals.

Extrapolation.
The process of making a guess or inference about what value(s) might lie beyond a set of observed values.

Fallacy of affirming the consequent.
See
Converse error
.

Framing.
The way in which a statistic is reported—for example, the context provided or the comparison group or amalgamating used—can influence one’s interpretation of a statistic. Looking at the total number of airline accidents in 2016 versus 1936 may be misleading because there were so many more flights in 2016 versus 1936—various adjusted measures, such as accidents per 100,000 flights or accidents per 100,000 miles flown, provide a more accurate summary. One works to find the true frame for a statistic, that is the appropriate and most informative one. Calculating proportions rather than actual numbers often helps to provide the true frame.

GIGO.
Garbage in, garbage out.

Incidence.
The number of new cases (e.g., of a disease) reported in a specified period of time.

Inductive.
A form of inferential reasoning in which a set of particular observations leads to a general statement.

Interpolation.
The process of estimating what intermediate value lies between two observed values.

Inverse error.
An invalid type of deductive reasoning of the form:

If A, then B

Not A

Therefore, not B

Mean.
One of three measures of the average (the central tendency of a set of observations). It is calculated by taking the sum of all observations divided by the number of observations. It’s what people usually are intending when they simply say “average.” The other two kinds of averages are the median and the mode. For example, for {1, 1, 2, 4, 5, 5} the mean is (1 + 1 + 2 + 4 + 5 + 5) ÷ 6 = 3. Note that, unlike the mode, the mean isn’t necessarily a value in the original distribution.

Median.
One of three measures of the average (the central tendency of a set of observations). It is the value for which half the observations are larger and half are smaller. When there is an even number of observations, statisticians may take the mean of the two middle observations. For example, for {10, 12, 16, 17, 20, 28, 32}, the median is 17. For {10, 12, 16, 20, 28, 32}, the median would be 18 (the
mean
of the two middle values, 16 and 20).

Mode.
One of three measures of the average (the central tendency of a set of observations). It is the value that occurs most often in a distribution. For example, for {100, 112, 112, 112, 119, 131, 142, 156, 199} the mode is 112. Some distributions are bimodal or multimodal, meaning that two or more values occur an equal number of times.