Welcome to Really Simple Statistics (RSS). There are lots of places online where you can ponder over the minute details of complicated equations but very few places that make statistics understandable to everyone. I won’t explain exceptions to the rule or special cases here. Let’s just get comfortable with the fundamentals.
What is a p value?
P value is a short form for probability value and another way of saying significance value. It refers to the chance that you are willing to take in being wrong. (I know, once in your life is too many times to be wrong.)
No matter how careful you are, random chance plays a part in everything. If you try to guess whether you’ll get heads or tails when you flip a coin, your chance of guessing correctly is only 50%. Half the time, you’ll flip tails even if you wanted to flip heads.
In research, we don’t like 50/50 odds. We instead only want to risk that 5% or 1% of our predictions are wrong. And, if you just picked 1% or 5%, you’ve just picked a peck of picked peppers. Whoops, I mean you’ve just picked a p value.
P values are almost always expressed out of 1. For example, a p value of 0.05 means you are willing to let 5% of your predictions be wrong. A p value of 0.1 means you are willing to let 10% of them be wrong. Don’t let that pesky decimal place fool you. A p value of 0.01 means 1% and a p value of 0.1 means 10%.
When you do a statistical test in software like SPSS or Systat, it will tell you the exact p value associated with your specific set data. For instance, it might indicate that the p value of your result is 0.035, or “Men are significantly taller than women, p=0.035.” That means there is a 3.5% chance that men are NOT actually taller than women and this result happened only because of random chance.
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