Misusing statistics isone of the most powerful ways to lie.
Sometimes evil is justified, and other times, knowing evil means knowing how to beat it.
Check out our evil week tag page.
The larger and more accurate the sample,the more precise your conclusions can be.
If the sample data you gather is bad, youll end up with false conclusions no matter what.
For example, when we ask our readers questions like Whats your favorite texting app?
we only get responses from people who choose to read Lifehacker.
For example, a cable news online grid might poll its viewers about a political candidate.
For example, if a survey on sexual activity asked Have you ever cheated on your spouse?
While common, theyre fundamentally biased because they dont attempt to control the input in any meaningful way.
For example, online polls that just ask you to click your preferred option fall under this bias.
While they can be fun and useful, theyre not good at objectively proving a point.
These are just some of themany, many ways that a sample can be biased.
If you want to create a misleading impression, well pick your poison.
So just Google around until you find an unscientific poll you like, or heckcreate your own.
If you wanted to twist a statistic to suit your needs, fudge the math.
To demonstrate the flaws in analyzing data, statisticianFrancis AnscombecreatedAnscombes quartet(diagramed above).
It consists of four graphs that, when viewed on a chart, show wildly different trends.
The X1 chart shows a basic scatter plot with an upwards trend.
X2 shows a curved trend that was going up, but is now going downward.
X3 shows a smaller trend upwards, but with one outlier on the Y axis.
Heres where it gets crazy.
If you show only the the text, youd see they made the same average salary!
If you create your charts properly, they should suggest ideas that correspond to reality.
Is it number of procedures?
Amount of money spent on procedures?
You dont have to.
Technically each line is going in the right direction, but the scaling is all kinds of wrong.
For example, in 2009 theU.S.
This could account for the decrease in cancer screenings.
This is how you accurately present information in its proper context!
So if you want to mislead people, all it takes is a little chart fudging.
News sites shouldlink to the studies or research theyre quoting(not articles about the studies).
Stop them outside the mall?
Was it a Twitter poll?
The method that you use to gather your data might point to (or disprove) sampling bias.
Reports can get outdated fast and trends can change over time.
Including the time frame that data comes from can say a lot about the conclusions you draw.
A tobacco company study claiming cigarettes are safe might not be correct unless someone else can verify it.
Sourcing isnt just used to avoid bias, but to give others the chance to verify your claims.
It opens your data, your methods, and your conclusions up to criticism.
It lets other venture to poke holes in your ideas.
If your conclusions cant stand up to criticism, then they fall apart.
The most accurate statistics are the ones that others can see and corroborate with their own research.
However, if your goal is to mislead yourself or someone else, dont bother sharing the sources.
In fact, your best defense is to just say Look it up!
No one can disprove that.
Illustration by Angelica Alzona.
Photos byWikimedia Commons,Americans United For Life, andQuartz.
