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Sample Size Formula for Estimating Mean:
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Sample size calculation determines the number of participants needed in a study to detect a statistically significant effect. The formula presented here is used for estimating a population mean with a specified confidence level and margin of error.
The calculator uses the sample size formula for estimating mean:
Where:
Explanation: This formula calculates the minimum number of observations needed to estimate a population mean within a specified margin of error with a given confidence level.
Details: Proper sample size calculation ensures studies have adequate power to detect effects, prevents wasting resources on underpowered studies, and provides valid and reliable results that can be generalized to the population.
Tips: Enter the Z-score based on your desired confidence level (1.96 for 95% confidence, 2.58 for 99% confidence), the estimated population standard deviation, and your desired margin of error. All values must be positive numbers.
Q1: What are common Z-score values?
A: For 90% confidence: 1.645, for 95% confidence: 1.96, for 99% confidence: 2.58.
Q2: How do I estimate the standard deviation?
A: Use data from previous studies, pilot studies, or published literature. If unavailable, use a conservative estimate based on the expected variability.
Q3: What is an appropriate margin of error?
A: Typically between 1-10% of the mean value, depending on the precision required for your study objectives.
Q4: When should I use this formula?
A: Use this formula when your primary objective is to estimate a population mean (continuous data) with a specified precision.
Q5: What if my population is finite?
A: For finite populations, use the finite population correction: \( n_{adjusted} = \frac{n}{1 + \frac{(n-1)}{N}} \) where N is the population size.