Compute the minimum number of necessary samples for a research project using this sample size calculator. Input your data below.

The number of respondents who take your survey is a sample size. It's a sample because it represents a part of the total group of people whose opinions or behavior you care about. As an example, you can select at random 10 out of 50 employees from a department at your job. Those 10 are the sample and the 50 are the population.

There can be two different sample sizes. One based on an infinitely large population, the other based on a smaller finite population. This finite number you can specify above.

The bigger the population is, the bigger the sample will need to be to accurately reflect the population.

Population Size: This is the total number of people in the group you are trying to reach with your survey. If you were taking a random sample of people in the United States, then your population size would be about 321 million.

Confidence Level: A measure of how confident you are that your sample accurately reflects the population, including room for the margin of error. Common standards used by researchers are 90%, 95%, and 99%.

If your confidence level is 95% in the above example, you could say you're 95% certain that between 38% and 42% of the United States Population do not like their jobs.

Margin of Error (Also called percent error): A percentage that describes how closely the answer your sample gave is accurate if you were to ask the entire population. For example, let’s say you send a survey to 500 people in the United States asking them if they like their jobs. 40% say no. If your margin of error is 2% you could say you're confident the true answer is somewhere between 38% and 42%. "How" confident you are can also be described as a percent and this is called a confidence Level.

See our margin of error calculator for how to calculate your percent error.

Population Proportion: This can be described as the makeup of the population. For example, if it's well known that 60% of college students are female, you could say the population proportion of college students is 60% female. If you wanted to mainly get opinions of college females, you would use this 60 percent in the formula below (for P). Most times though these numbers are not known and 50% (.50) is used for P. This .5 number produces the largest possible sample size, as it is most conservative.

Finite Population Adjustment: If you know the exact population number for the group you are targeting, an adjustment to sample size will be made to reflect this population number. See the below formulas.

Sample Size Equation

The z-score is the number of standard deviations a given proportion is away from the mean. To find the correct z-score to use in your calculation, refer to this table:

Desired Confidence Interval	Z-score
80%	1.28
85%	1.44
90%	1.65
95%	1.96
99%	2.58

Adjusted Sample Size Equation

The sample size calculator is typically used during the planning phase of a study to estimate how many responses are required for reliable results. Below are common applications where sample size planning is critical.

Before committing time or budget, teams estimate how many responses are required to produce reliable results and determine whether a study is realistic given participant access and timelines. This is especially common in early-stage or exploratory work, where a feasibility study helps decide whether a full study should move forward.

When running a demographic survey or collecting baseline population data, sample size helps ensure results reasonably represent the target audience. Calculating sample size in advance provides clarity on how many responses are needed to support demographic breakdowns, such as age, location, or role, without producing unstable or misleading estimates. Using a structured demographics survey template alongside proper sample size planning improves consistency and interpretability.

Sample size planning is closely tied to research questions. Clearly defined research questions determine what needs to be measured, how precise results must be, and whether subgroup comparisons are feasible. Estimating sample size upfront helps align question design, distribution strategy, and data collection effort so results are reliable enough to support meaningful analysis and comparison.