People will lie to you to get your money. They'll lie to you to try to convince you to believe or follow them. They'll lie to you to build themselves up. They'll lie to you to win an argument.
And we know we'll lie to ourselves. We want to justify / support our beliefs. We, obviously, want to prove ourselves right / smart.
Perhaps the most insidious form currently in vogue is through manipulated but impressive-looking mathematics.
This has been long known, of course. Thus the saying: Lies, damn lies, and statistics.
Naked Statistics: Stripping the Dread from the Data, by Charles Wheelan -- an engaging, funny, well-written survey of probability and statistics.
For example, in the chapter, The Importance of Data, Professor Wheelan covers the many forms of bias that sneak into data: Selection Bias, Publication Bias, Recall Bias, Survivorship Bias, and Healthy User Bias. From the latter:
"People who take vitamins regularly are likely to be healthy -- because they are the kind of people who take vitamins regularly! Whether the vitamins have any impact is a separate issue. Consider the following thought experiment. Suppose public health officials promulgate a theory that all new parents should put their children to bed only in purple pajamas, because that helps stimulate brain development. Twenty years later, longitudinal research confirms that having worn purple pajamas as a child does have an overwhelmingly large positive association with success in life. We find, for example, that 98 percent of entering Harvard freshmen wore purple pajamas as children (and many still do) compared with only 3 percent of inmates in the Massachusetts state prison system.
"Of course, the purple pajamas do not matter; but having the kind of parents who put their children in purple pajamas does matter. Even when we try to control for factors like parental education, we are still going to be left with unobservable differences between those parents who obsess about putting their children in purple pajamas and those who don't. As New York Times health writer Gary Taubes explains, "At its simplest, the problem is that people who faithfully engage in activities that are good for them--taking a drug as prescribed, for instance, or eating what they believe is a healthy diet--are fundamentally different from those who don't." This effect can potentially confound any study trying to evaluate the real effect of activities perceived to be healthful, such as exercising regularly or eating kale. We think we are comparing the health effects of two diets: kale versus no kale. In fact, if the treatment and control groups are not randomly assigned, we are comparing two diets that are being eaten by two entirely different kinds of people. We have a treatment group that is different from the control group in two respects, rather than just one."