For the population of people in the U.S., a parameter never changes, but it is not always describing the entire population. For example, if you are looking at statistics about age and gender for women in America, that would be a parameter because it is only talking about women in America; whereas if you were looking at the average height for all Americans regardless of gender or age then that would be a statistic because it includes everyone who lives here. You can find parameters by using simple formulas to calculate them from other sets of data, such as dividing the total number by sample size to find an average or calculating how many are left after subtracting out those who did not respond to your survey question.
It is important to distinguish between the parameters of a population and statistics that describe it because they are not always interchangeable. The parameter of a population does not change, whereas stats can appear differently for different populations depending on what you’re measuring. That’s why when looking at something like age and gender data about American women from 30-40 years ago, those numbers do not mean much in today’s society anymore; but if one were to look at average height then it would still be valuable information. For example, even though there is more diversity in America than ever before and “race” used to be seen as only white or black (with many people who fall into both categories), now there are so many more options including Hispanic/Latino(a), Asian, Native American (including Eskimo and Aleut). Stats are data points that describe a population because they are not always interchangeable.
For instance, the average height of an American woman is 164 cm., according to the World Health Organization website This site also lists some other interesting statistics: “The percentage of overweight adults worldwide has more than doubled since 1980.”
The point here is that whereas the average height for an American woman may be 164 cm., it does not make sense to use this statistic as a parameter to describe all of the women in America. For example, if there were more Asian-Americans who are smaller than average (such as those listed on the World Health Organization website), then using the same number would only represent some Americans and not others. This distinction between parameters and statistics can also be applied when studying different populations, such as age groups or gender identifications; it’s important to know which one you want to study before defining your population because they’re not interchangeable either.
For instance, imagine we have two sets:
set A with 20 people under 50 years old, and set B with 100 people under 50 years old. If we were to study the parameter of height, then it would be possible for each person from both sets to have a different height without contradicting the other. For example, in set A there may be one person who is 164 cm., while in Set B there might not be anyone as tall or even close (this has nothing to do with whether they are at least 50 years old).
However, if we wanted to study how many people needed glasses because their nearsightedness was over -0.75D (-25/200), this statistic could only apply when looking at those within the same age groups – so someone from set A wouldn’t count just because they’re under 50.
The parameter of height applies to all people within a set, no matter what their gender or age is – and this would be true for any other variable as well. Whereas statistics are only relevant when you’re looking at the same group of people. For example, if we were looking at how many children under 12 years old get cavities in one year (statistic) then that statistic would apply to those who have been born between 2000-2020 or something like that specific generation/age range.
This distinction becomes important because it’s possible for two sets to have different parameters but the same statistic: let’s say there was another population with 50% women and 50% men; both groups might have an average height of 175 cm., but the parameters would be completely different.
The parameter of a population refers to any one or more aspect which is measured in order to find out what it’s like and how it behaves; whereas statistics are figures that have been calculated from data about the same group over time, and they may change depending on who you’re measuring (age range). For example, let’s say we wanted to know the average height for people within this set: 175 cm. This could be an important statistic because it will tell us whether our team can design clothes based on a common body type.