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Why Study Statistics?


"One distinguishing characteristic of an educated person is that he or she can be emotionally moved by statistics." George Bernard Shaw
"Mankind would rather commit suicide than learn arithmetic." Bertrand Russell

Statistics is a tool used by many disciplines. You use statistics to help you answer questions about phenomena of interest. Thus the data you collect isn't just numbers--it is information which can help you understand the phenomenon of interest. Different disciplines have different phenomena they seek to understand. As a psychologist, I'm interested in understanding particular behaviors, those are the phenomena of interest I'm going to use statistics to help understand.

In this course statistics is taught by using example of various phenomena of interest. You'll be collecting data and then using statistical procedures to help understand the phenomenon you have explored.

Statistics is power--the power to persuade*. Statistics provides a new way of marshaling information to make a decision. Those who have discovered the manner of quantitative analyses are less swayed by proficient persuasive compelling speakers who weave their charismatic magic around the issue. Its the opposite of "Don't confuse me with the facts." -- Its "Give me the facts in a way I can understand them."

Generally speaking, when we have a data set and we want to figure out what the data tells us about the phenomena, we are doing "data analysis." Statistics is only one component of data analysis.

Two varieties of statistics

Inferential Statistical Reasoning Explored Further.

Basis of inferential statistics.

Note: An extended tutorial expanding this topic is available. Click here.

O.K. Let's pretend I have collected the data. I found the SVRT for 10 males and 10 females and found the average RT of women was 0.02 seconds longer (the difference in the means was 0.02 seconds).

Now the question is whether or not the same type of difference--the mean female SVRT is longer than the mean male SVRT--will repeat itself with a new group of subjects.

Here is how inferential statistics answers that question.

  • Assume the real difference between male and female RTs is zero (0.0) seconds.
  • Use computers to simulate running that experiment many thousands of times using the situation where there is NO difference between female and male reaction times.
  • Where does the data I actually measured fit in the simulated data?

Here's an example of what how that kind of information might be plotted:

Each computer simulation result is plotted as a filled square. As you can see the filled squares bounce around the "no difference" value.

Where does the actual data fit in this picture? I've drawn two possibilities. It could be the open square or it could be the open triangle.

  • If the actual data is located like the open square then the conclusion I would make is "The data I got (mean difference is 0.02 sec) is the kind of result I'd expect when really there is NO difference so I cannot say there is a difference."
  • If the actual data is located like the open triangle then the conclusion I would make is "The data I got is NOT like the data that happens when the real difference is zero, so my data is showing there is a REAL difference between male and female reaction times."

What the open triangle illustrates is a "whopper" effect. Its an easy decision to make with data like that. But what if the triangle were closer to the filled squares? How would you make the decision then? Well, inferential statistical procedures provides some rules for reaching the decision of whether or not the "real" results is similar to the "simulated" results.


* I was introduced to the "statistics is the power to persuade" argument many years ago and I neglected to credit the originator. Now his name is lost in the vagarities of memory. Belated thanks.


© 2002 - 2007 by BurrtonWoodruff. All rights reserved. Modified Sunday, March 25, 2007