Learn about different terms about taking samples and different examples!
Statistics is full of taking samples. This packet has descriptions of different sample designs. These are useful to know outside of the classroom, too.
For when populations are just too big...
Often, samples can become BIASED, or slanted towards a particular answer or outcome that does not
reflect the truth. Bias is sometimes seen in news sources, books, and websites that only present one side of
the story. But the bias we are interested in isn't about reporting, but inaccurate numbers.
Samples can be biased fro a number of reasons. SAMPLING BIAS is choosing a sample in which data is
more likely to be chosen then others. Look at the example in the Powerpoint: if you are taking a survey trying
to find the percent of people who like sandwiches, the answer will be biased if you conduct the survey outside
of a Subway. Another example is a survey in which people can choose to participate--The results would have a
disproportionately low amount of antisocial or shy people, and a high amount of strongly opinionated or outgoing
No sample is perfect, they all have some bias. Part of being a good statistician is learning to eliminate as
much bias as possible, in order to obtain accurate results.
Watch and learn!