A statistics professor was describing sampling theory to his class, explaining how a sample can be studied and used to generalize to a population.
One of the students in the back of the room kept shaking his head.
“What’s the matter?” asked the professor. “I don’t believe it,” said the student, “why not study the whole population in the first place?”
The professor continued explaining the ideas of random and representative samples.
The student still shook his head. The professor launched into the mechanics of proportional stratified samples, randomized cluster sampling, the standard error of the mean, and the central limit theorem.
The student remained unconvinced saying, “Too much theory, too risky, I couldn’t trust just a few numbers in place of all of them.”
Attempting a more practical example, the professor then explained the scientific rigor and meticulous sample selection of the Nielsen television ratings which are used to determine how multiple millions of advertising dollars are spent.
The student remained unimpressed saying, “You mean that just a sample of a few thousand can tell us exactly what over 250 MILLION people are doing?”
Finally, the professor, somewhat disgruntled with the skepticism, replied, “Well, the next time you go to the campus clinic and they want to do a blood test…tell them that’s not good enough … tell them to take ıt all!!”