# Sample size

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− | Sample size is useful when talking about real-world experimentation using the scientific method. Sample size is the size of the participants in an experiment or number of trials of an experiment. | + | Sample size is useful when talking about real-world [[Experiment|experimentation]] using the [[scientific method]]. Sample size is the size of the participants in an experiment or number of trials of an experiment. |

Sample size is important when researching the results of a scientific study or designing an experiment. One wants a sample size to apply to the entire world or universe, but in practice its impossible. So, typically a sample size will be made up of enough data to be statistically relevant which then can be applied in practice. | Sample size is important when researching the results of a scientific study or designing an experiment. One wants a sample size to apply to the entire world or universe, but in practice its impossible. So, typically a sample size will be made up of enough data to be statistically relevant which then can be applied in practice. | ||

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Example: Ten people take a new pill to relieve their back pain and 3 people get better and 7 don't. Another ten people take a placebo for their back pain and two get better. The actual medicine has a ten percent effectiveness rate over placebo. One would consider this to be an effective drug over the placebo but look at the sample size for the experiment, there were a total of twenty people in the experiment. People who declare back pain as a regular symptom in their daily life are much more numerous than just twenty people in one experiment. When this data would be statistically analyzed, it would be found to have a high variance. A high variance is bad for the results of an experiment. | Example: Ten people take a new pill to relieve their back pain and 3 people get better and 7 don't. Another ten people take a placebo for their back pain and two get better. The actual medicine has a ten percent effectiveness rate over placebo. One would consider this to be an effective drug over the placebo but look at the sample size for the experiment, there were a total of twenty people in the experiment. People who declare back pain as a regular symptom in their daily life are much more numerous than just twenty people in one experiment. When this data would be statistically analyzed, it would be found to have a high variance. A high variance is bad for the results of an experiment. | ||

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## Revision as of 19:20, 30 November 2010

Sample size is useful when talking about real-world experimentation using the scientific method. Sample size is the size of the participants in an experiment or number of trials of an experiment.

Sample size is important when researching the results of a scientific study or designing an experiment. One wants a sample size to apply to the entire world or universe, but in practice its impossible. So, typically a sample size will be made up of enough data to be statistically relevant which then can be applied in practice.

What can happen if a sample size is too small? - Data can be skewed towards a particular positive or negative result without any real applicable statistical significance.

Example: Ten people take a new pill to relieve their back pain and 3 people get better and 7 don't. Another ten people take a placebo for their back pain and two get better. The actual medicine has a ten percent effectiveness rate over placebo. One would consider this to be an effective drug over the placebo but look at the sample size for the experiment, there were a total of twenty people in the experiment. People who declare back pain as a regular symptom in their daily life are much more numerous than just twenty people in one experiment. When this data would be statistically analyzed, it would be found to have a high variance. A high variance is bad for the results of an experiment.