Tag Archives: Sample

SNOWBALL SAMPLES

SNOWBALL

(The following effort at statistical humor comes from S. Huck)

BACKGROUND AND BUBBA’S QUESTION:

True to the weather forecast, the college campus was being blasted by a heavy snowfall. Inside a small dining hall, students were eating, studying, talking, & texting. Suddenly, Bubba darted outside where he scooped up some of the white stuff, packed it together in his hands, and then quickly returned inside. Getting everyone’s attention, Bubba held up the cold, white sphere he had just made and said: “Hey, I learned about this in my stats course. Guess what it is?”

BUBBA’S ANSWER: “A snowball sample!”

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Beyond the Joke: Things worth knowing about snowball samples:

1. Definition: A snowball sample is formed during the time period when people are being recruited to serve as a study’s research participants. Through face-to-face contact or indirect methods (such as posted notices), the researcher successfully solicits certain individuals to voluntarily enter the study. Next, those initial volunteers are asked to recruit additional participants. This process—of existing volunteers recruiting new volunteers—continues until the desired sample size has been achieved.

2. Idea Behind the Name: Imagine a snowball rolling down a steep, snow-covered hill. At first, the snowball is small. But it gets larger and larger as it heads toward to the bottom of the hill. In a similar fashion, a snowball sample grows in size as volunteer participants successfully recruit additional participants.

3. When Used: Snowball samples are used mainly in studies wherein (1) the researcher doesn’t know who the potential participants are or how to contact them, or (2) potential volunteers are more likely to agree to be in a study if they are recruited by a “peer” rather than by an unknown researcher.

4. Example: In a research report entitled “The ‘Staying Safe’ Intervention: Training People Who Inject Drugs in Strategies to Avoid Injection-Related HCV and HIV Infections” (from the journal: AIDS EDUCATION AND PREVENTION), the researchers stated that “Snowball sampling of participants began with eight participants directly recruited from two sources…. These eight participants then recruited 60 eligible peers.”

5. Quality: Because of the way snowball samples are formed, it is difficult to generalize information about them to larger populations. (Such generalizations are much easier to make with stratified random samples and other kinds of samples classified as “probability samples.”) Thus, snowball samples are most useful in studies wherein (1) the goal is to generate rather than confirm hypotheses or (2) the participants, collectively, are considered to be the target group of interest.

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RANDOM SAMPLES: WHAT DO THEY LOOK LIKE?

Extract a perfectly random sample from a population, and what will you get? Many people think such a sample will be just like the population, but smaller. They expect the sample to be a “miniature population.” Bad thought. Most likely, the numerical characteristics of the sample will not match exactly those of the population.

If you randomly select 16 people out of a population containing as many males as females, what kind of gender split should you expect in the sample? Don’t predict 8 males & 8 females! That’s because the odds are about 4-to-1 AGAINST having the sample be perfectly balanced gender-wise.

To prove to yourself that a sample is not likely to end up being a small mirror-image of the population, conduct a little coin-flipping experiment. You can do this quickly via a computer simulation available through the link below. After you get to the “Simulating Coin Tossing” applet created by Allan Rossman & Beth Chance, click on the gray, rectangular button labeled “16 Tosses.” This will cause the computer to (a) flip 16 fair coins, (b) show you, simultaneously, the result of each flip, and (c) put a dot in the graph to indicate the number of heads contained in the sample. Click the “16 tosses” button several more times, and watch what happens as additional dots are put into the graph. You will see, especially after clicking the “Show Tallies” button, that the majority of samples produced something other than 8 heads and 8 tails, even though the coins being flipped were fully unbiased.

Coin-Flipping Simulation

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Filed under Misconceptions