Introduction to Critical Thinking

An introduction to faulty thinking, and why it's important to recognize it when you see it.

Ad hominem -- attacking rather than arguing

Appeal to authority -- why credentials and fame don't mean you're right about everything

Appeal to probability -- risk doesn't mean certainty

Circular reasoning -- assuming your conclusion

Confirmation bias -- thin evidence + prior belief ≠ sound reasoning

Dart-thrower's bias -- noticing the hits, ignoring the misses

Emotionally-charged language -- being swayed by your feelings

False analogy -- comparing two things that really aren't much alike

If-by-whiskey -- arguing both sides of a claim simultaneously

Motive fallacy -- a motive doesn't establish truth

Moving the goalposts -- shifting your ground

Naturalistic fallacy -- "natural" doesn't mean it's good for you

Negative proof fallacy -- lack of proof doesn't prove anything

No true Scotsman -- shifting your definition when you're challenged

Red herring -- tossing in an irrelevant fact to confuse matters

Single-cause fallacy -- complex events usually have complex causes

Straw man -- arguing against a weak or misrepresented version of a claim

Sunk-cost fallacy -- something doesn't become a good idea because you've put a lot of time and money into it

Tu quoque -- "you did it, too!"

Weasel words -- weak verbiage meant to avoid challenge

Some examples from media, giving you an opportunity to try your hand at picking out the fallacies we've studied.  Some of them have more than one possible answer!

How to recognize valid arguments and begin to understand proper construction of an argument that is both valid and true.

Some characteristics of valid arguments, and how to put together premises to draw a conclusion.

A few of the limitations of deductive reasoning -- and why validity isn't enough to establish truth.

An introduction to dealing with data, numbers, statistics, and graphs -- and how to recognize when you're being lied to or misled.

The danger of relying on averages, and why a large sample size is more reliable than a small one.

The hazard of large data sets -- seeing spurious patterns in random numbers.

The difference between seeing a correlation (which is easy) and establishing a causative relationship (which is not so easy).

Why the "some is good, so more must be better" rule doesn't work.

Why trend lines can mislead, and you should always look at the scatter in the data, not just the overall trend.

A chance for you to try your hand at applying some of the statistical rules-of thumb to some real world examples.

Why it's important to know some science, even if you're a layperson.  Maybe especially if you're a layperson.

A few areas in science that are commonly misunderstood or misrepresented.

There's no getting around the fact that to understand science, you have to speak the language.  Here are a few scientific terms that are consistently misused -- and a look at what the real definitions are.

Now that you know all this, what do you do with it?

Have you been exposed to this scary toxin?  Find out the straight scoop here!