# Choose any example that illustrates incorrect application of statistical fundamentals. The example may be from current events, your professional life, your personal life, and so on. Describe how the current understanding/approach violates statistical fundamentals and what could/should be done differently.

The core aspect of this question calls upon the writer to identify common pitfalls in applying statistical knowledge to a variety of arenas in life. The first step to analyzing potential missteps and misuses (unintentional or intentional) is to think of a few foundational elements of statistics and envision how they may unintentionally be abused or intentionally misused to lead a reader (or the general public).

Here are a few core elements that help ensure statistical validity to get your ideas flowing:

• representativeness of a sample with the appropriate population
• techniques to test reliability
• appropriate use of visualization for statistics collected

We can now think of some examples in each of those life areas where the above elements could be misused or manipulated.

In your personal life, you might mistakenly think a statistical figure applies to you if you are misinformed about the relevant population (for example, if you have X disease and see a statistical figure that 80% of patients with Y disease will die within five years, it would be a mistake to apply that figure to yourself without further information).

Professionals frequently rely on data to make informed decisions. Say you're trying to apply techniques to test the reliability of your statistical measuresâ€”which do you choose? Most techniques in statistics are only applicable and mathematically sound in specific scenarios. Each relies on a variety of assumptions that need to be true in order for the result to be reliable. If you choose the wrong technique for your statistical measures, you may get unintended consequences.

One of the most relevant examples related to current events is mislabeling data visuals to influence how readers absorb data. This could be as blatant as abbreviating one axis on a graph to make a trend appear more drastic, or it could be implying causation between two phenomena by juxtaposing them together as if data shows they directly affect each other.

As you can see, there are numerous examples of statistical abuse that can crop up in life. Think about any of these examples in your day-to-day life and try to see if you can find instances where you've encountered these or other misuses.