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When conducting research, it is important to have a large enough sample size to make accurate statistical inferences. A sample size that is too small can lead to inaccurate conclusions, while a sample size that is too large can be a waste of resources.

Confidence Level (Î±): | |

Margin of Error (e): | % |

Population Proportion (p): | % |

Population Size (N) (optional) |

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The formula for determining the minimum sample size needed for a study is:

n = (Z_{α/2} / E)^{2} * p * (1 - p)

where:

- n = minimum sample size
- Z
_{α/2}= critical value of the standard normal distribution at a given level of significance (such as 1.96 for a 95% confidence level) - E = margin of error
- p = estimated proportion of the population with the desired characteristic

Suppose a researcher wants to estimate the proportion of people in a certain town who are vegetarian. They want to have a 95% confidence level with a margin of error of 5%.

The researcher can use the sample size calculator to determine the minimum sample size needed for the study:

- Z
_{α/2}= 1.96 (for a 95% confidence level) - E = 0.05 (for a margin of error of 5%)
- p = unknown

The researcher may estimate the proportion of vegetarians to be 50%, which would yield the maximum sample size required:

n = (1.96 / 0.05)^{2} * 0.5 * (1 - 0.5) = 384.16

Therefore, the researcher needs at least 385 people to participate in the study in order to obtain results with a 95% confidence level and a margin of error of 5%.

The sample size is a crucial component of research design. It affects the accuracy and reliability of statistical inferences made from the sample to the population. A small sample size may lead to imprecise and unreliable estimates, while a large sample size may result in unnecessary expenses and time.

Thus, the appropriate sample size should be determined based on the research question, the variability of the population, the level of confidence desired, and the margin of error acceptable.

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