What is ‘Sample Size’?
‘Sample size’ is a market research term used for defining the number of individuals included to conduct research. Researchers choose their sample based on demographics, such as age, gender, or physical location.
Samples can be vague or specific. For example, you may want to know what people within the 18-25 age range think of your product. Or, you may only require your sample to live in Nigeria, which gives you a wide range of the population. The total number of individuals in a particular sample is the sample size.
Why do you need to determine sample size?
Let’s say you are a market researcher in Nigeria and want to send out a survey or questionnaire. The purpose of the survey is to understand your audience’s feelings toward a new cell phone you are about to launch. You want to know what people in Nigeria think about the new product to predict the phone’s success or failure before launch.
Hypothetically, you choose the population of Lagos, which is 17,552,940. You use a sample size determination formula to select a sample of 400 individuals that fit into the consumer panel requirement. You can use the responses to help you determine how your audience will react to the new product.
However, knowing how to determine a sample size requires more than just throwing your survey at as many people as you can. If your sample size is too big, it could waste resources, time, and money. A sample size that’s too small doesn’t allow you to gain maximum insights, leading to inconclusive results.
What are the terms used around the sample size?
Before we jump into sample size determination, let’s take a look at the terms you should know:
- Population Size: Population size is how many people fit your demographic. For example, you want to get information on doctors residing in South West, Nigeria. Your population size is the total number of doctors in South West, Nigeria. Don’t worry! Your population size doesn’t always have to be that big. Smaller population sizes can still give you accurate results as long as you know who you’re trying to represent.
- Confidence Level: Confidence level tells you how sure you can be that your data is accurate. It is expressed as a percentage and aligned to the confidence interval. For example, if your confidence level is 95%, your results will most likely be 95% accurate.
- The Margin of Error (Confidence Interval): When it comes to surveys, there’s no way to be 100% accurate. Confidence intervals tell you how far off from the population means you’re willing to allow your data to fall. A margin of error describes how close you can reasonably expect a survey result to fall relative to the real population value.
- The Sample Proportion: It is an estimated proportion of an attribute that is present in a population. It varies from sample to sample in a way that cannot be predicted with certainty. A proportion of 50% indicates a greater level of variability than that of 20% or 80%. This is because 20% and 80% indicate that a large majority do not or do, respectively, have the attribute of interest. By default, sample proportion denoted by p is considered as 0.5 because it indicates the maximum variability in a population. It is often used in determining a more conservative sample size.
OpenBook Yamane Calculator
OpenBook Yamane Calculator (OY Calculator, 2022) is a free online calculator developed to help research students or other researchers from various fields, having problem in calculation using Taro Yamane Formula manually, determine sample size accurately. To use the calculator, click here.
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