{"id":27742,"date":"2024-06-25T00:00:00","date_gmt":"2024-06-25T04:00:00","guid":{"rendered":"https:\/\/www.hydroassoc.org\/research-101-the-importance-of-sample-size\/"},"modified":"2024-10-25T13:30:01","modified_gmt":"2024-10-25T17:30:01","slug":"research-101-the-importance-of-sample-size","status":"publish","type":"post","link":"https:\/\/www.hydroassoc.org\/research-101-the-importance-of-sample-size\/","title":{"rendered":"Research 101: The Importance of Sample Size"},"content":{"rendered":"\n\n\t

\"SampleIn the Research 101 blog series<\/strong>, we have discussed how clinical trials are designed<\/a> and the strengths and weaknesses of these designs. One factor that can derail or strengthen any study is Sample Size.<\/p>\n

So, what is Sample Size<\/strong>? Very simply stated, sample size is the number of people participating in a study<\/strong>. The more people that participate, the better the study is. Having a large number of participants reduces the risk of accidentally having extreme, or biased, groups – such as having all adults or all children in a study that should have equal numbers of adults and children.<\/p>\n

Let’s find out why:<\/h2>\n

Pretend that we have a jar with blue and white marbles. The jar represents our entire population. Half of the marbles are White <\/strong>and half are Blue<\/strong>.<\/p>\n

White<\/strong> marbles = adults = \u00bd of the population is adults<\/p>\n

\u00a0 \u00a0 Blue<\/strong> marbles = children = \u00bd of the population is children<\/p>\n

We want a representative sample of people in our study – so ideally we want half of the participants to be adults and half to be children. Unfortunately, we decide that we only need 3 people in our study<\/p>\n

\u00a0We pick 3 people at random (sample size = 3)<\/strong><\/p>\n\"\"\n

\"Jar<\/p>\n\"\"\n

There are 4 possible color combinations<\/strong>.<\/p>\n

\"Possible<\/p>\n

 <\/p>\n\"\"\n


We need to come up with a better plan<\/h2>\n

After looking at the odds of randomly picking an extreme, non-representative group, we decide that we need a much bigger sample size.\u00a0 This time, we are going to choose 50 people to be in our study (sample size = 50). With a sample size of 50, there are 51 possible combinations of Blue and White marbles.<\/p>\n

When 50 people are randomly chosen, there is only a 3.9% chance of having an<\/strong> extreme group<\/strong> composed of either all adults (White) or all children (Blue).\u00a0 Therefore, we are likely to pick a group that is more representative of our population.<\/p>\n

This is a simple example illustrating one-way sample size is important for research studies. In real-life situations, many issues associated with extreme, or biased, groups can be avoided by increasing the number of people in each group.<\/p>\n

Other entries in our Research 101 Blog Series with Dr. Koschnitzky:<\/h5>\n

Research 101: An Explanation of Clinical Trials Design<\/a><\/p>\n

Research 101: Randomization<\/a><\/p>\n

Research 101: Junction, Junction, What’s Your Function?<\/a><\/p>\n

Research 101: Generalizability<\/a><\/p>\n\n\r\n

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