Usually we cannot collect data from every member
of the population in which we are interested. We must find a sample that will
accurately represent that population. We use the results of the sample to draw a conclusion about the population.
Here are some different types of samples:
biased sample: A
sample that does not accurately represent the population. Biased samples can
give inaccurate results. For example, if you wanted to know the percentage of
students at your school who recycle, sampling from the ecology club would result in a
biased sample.
convenience sample: A
sample that is chosen because it is easy. For example, if you wanted to find out
the percentage of students who ate the school lunch, and you just asked those
people at your table if they ate the school lunch, then you would have a
convenience sample.
random sample: A
sample that accurately represents the whole population. For example, if you
wanted to get a random sample of all the students in your school, you could put
all of their names in a bin and randomly draw out as many as you wanted in your
sample.
systematic sample: A
sample that is chosen systematically. For example, if you surveyed every tenth
person in a line, you would have a systematic sample.
voluntaryresponse sample: A sample you get when you ask for volunteers. For example, if you had a
survey on the internet, then those who answered would do so
voluntarily.
