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Estimating the Mean State Area

Alignments to Content Standards: 7.SP.A


The table below gives the areas (in thousands of square miles) for each of the “lower 48” states. This serves as the population for this study. Your task involves taking small samples from this population and using the sample mean to estimate the mean area for the population of states by following the steps indicated below.

Your challenge is to discover some important properties of random samples, properties that illustrate why random sampling is the key to getting good statistical information about a population. In this task, unlike “real life” situations, you will have all of the population data at hand, and will use it to see how random sampling works. Your classmates will have the same task, and you will be combining your data with theirs. In the first part of the task, you will select a sample of states by a method of your choice. For the second part you will follow a specified procedure.

Procedure #1: Choose your own sample

  1. By any quick method you like, select 5 states that you think represent the 48 (perhaps by tossing 5 grains of sand on a map of the states and selecting the states on which they fall; shutting your eyes and pointing your finger at a spot on the map, repeating the process until 5 states are selected; systematically selecting a state from the northeast, the south, the mid-west, all east of the Mississippi, and two states from west of the Mississippi).
  2. Find the areas of these 5 states and calculate the mean for your sample.
  3. As a class, construct a dot plot of the sample means.

Procedure #2: Use random sampling

  1. Number the 48 states from 1 to 48. Then use a random number table or a random number generator to obtain 5 random numbers between 1 and 48, and then find the states corresponding to these numbers.
  2. Find the areas of these 5 states and calculate the mean for your random sample.
  3. As a class, construct a dot plot of the sample means from the random samples.
  4. Compare the plots produced in steps c and f. Where are the centers? Which has greater spread?
  5. Repeat steps a - f for random samples of size 10 and compare the plots. What differences, if any, do you see in the plots? What feature or features appear to stay the same?
  6. Find the actual mean state area using the data from all the states. Summarize at least two important points concerning the value of random sampling.
State Area
Texas 269
California 164
Montana 147
New Mexico 122
Arizona 114
Nevada 111
Colorado 104
Oregon 98
Wyoming 98
Michigan 97
Minnesota 87
Utah 85
Idaho 84
Kansas 82
Nebraska 77
South Dakota 77
Washington 71
North Dakota 71
Oklahoma 70
Missouri 70
Florida 66
Wisconsin 65
Georgia 59
Illinois 58
Iowa 56
New York 55
North Carolina 54
Arkansas 53
Alabama 52
Louisiana 52
Mississippi 48
Pennsylvania 46
Ohio 45
Virginia 43
Tennessee 42
Kentucky 40
Indiana 36
Maine 35
South Carolina 32
West Virginia 24
Maryland 12
Massachusetts 11
Vermont 10
New Hampshire 9
New Jersey 9
Connecticut 6
Delaware 2
Rhode Island 2

IM Commentary

The task is designed to show that random samples produce distributions of sample means that center at the population mean, and that the variation in the sample means will decrease noticeably as the sample size increases. Random sampling (like mixing names in a hat and drawing out a sample) is not a new idea to most students, although the terminology is likely to be new. Most students readily grasp this as a “fair” way to select the sample because each item in the population gets an equal chance of being selected. Standard 1 uses the term “representative,” which has no technical definition in statistics but might now be interpreted as “unbiased” in the sense that the distribution of sample means centers right where you want it toat the population mean.


The plots displayed below show the areas of the population of 48 states, followed by three sets of 25 sample means each collected, first, through a process similar to the sand throwing idea given above, second, by random samples of size 5 and, third, by random samples of size 10. The population mean is 65 and the three means of the distributions of sample means are 97.5, 63.4 and 64.1, respectively. The two random samples center at the population mean, but the “sand” sample means do not. They are “biased” by the fact that a grain of sand has a greater chance of landing on a large state than on a small one.

The “sand” sample means also have larger variation than either or the random sampling methods. The random sampling means for samples of size 5 have much less variability than the population; the random sampling means for samples of size 10 have, in turn, less variability than do those for samples of size 5. (In fact, the variability of sample means scales down by a factor of $\frac{1}{\sqrt{n}}$, where n denotes the sample size. Samples of size 5 have less than half the variability of the population and samples of size 10 have less than a third of the variability of the population.)

In summary, random sampling produces sample means whose distributions center at the population mean and have variation that decreases as the sample size increases.





Harlan says:

over 2 years

So, there is the explanation of "sand" method but in the samples graphs above, the second one is called "rice" samples. I assume this refers to the "sand". Could this be verified somewhere?