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Sample size calculator

Data type

Population size

Population Proportion (%)

Margin of Error (%)

Confidence Level (%)

Calculate

Samples required

Data Types

The sample size formula is different for Discrete (Categorical) and Continuous (Numerical) Data.

Discrete or Categorical data can take on one of a limited, and usually fixed, number of possible values.
Examples : Yes/No, Day of the week, Month, Gender, etc.

Continuous or Numeric data is a number that can be measured on a continuum or scale and can be sub divided to more decimals.
Examples : Time, length, weight, etc.

Select this based on the type of data being analyzed

Population size

Total number of people that make up the target audience of your study or survey.

Population is the total number of people or number of items in scope. It would be the total customer base for customer satisfaction surveys or total employees for emplouyee satisfaction measurement.

20,000 gives a reasonable sample size for very large or infinite populations.

If unsure, use an approximate number or 20,000 for large populations.

Population Proportion

Number of members in the population with a particular attribute divided by the number of members in the population..

A population proportion, generally denoted by P is a parameter that describes a percentage value associated with a population. 50% value requires the highest sample size and sample reduces both ways from 50% equally (sample size at 0.3 = sample size at 0.7).
Example : If the expected % of literate people in a country is 85%, then population proportion is 85%.

Common practice is to use 50%, if unsure.

Margin of error

The amount of error that is acceptable in the result.

The larger the margin of error, the less confidence one should have on the closeness to the results of the population. A larger sample size produces a smaller margin of error, all else remaining equal.

Increasing the margin of error reduces sample size and reduces precision - and vice versa.
Example : a value of 60 with 5% margin of error indicates that expected value will be between 57 and 63.

Industry standard and common practice is 5%.

Confidence Level

The probability that the value of a parameter falls within a specified range of values.

The desired level of confidence is set by the researcher (not determined by data).
Most commonly, the 95% confidence level is used. However, other confidence levels can be used, for example, 90% and 99%.

Higher confidence level indicates higher probability of the result being representative of the population. Higher confidence level requires a larger sample size.

95% is the most commonly used confidence level.

Population standard deviation

The sample size formula is different for Discrete (Categorical) and Continuous (Numerical) Data.

Standard deviation (SD, also represented by the Greek letter sigma σ or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values
Standard deviation value divided by mean gives this value.

A low standard deviation indicates that the data points tend to be close to the mean of the set, while a high standard deviation indicates that the data points are spread out over a wider range of values.

Data Types

Select this based on the type of data being analyzed

Population size

If unsure, use an approximate number or 20,000 for large populations.

Population Proportion

Common practice is to use 50%, if unsure.

Margin of error

Industry standard and common practice is 5%.

Confidence Level

95% is the most commonly used confidence level.

Population standard deviation

If unsure, use 0.5 value.