Slovins’s formula is used to calculate an appropriate sample size from a population.
Statistics is a way of observing a population’s behavior by taking a sample. It’s usually impossible to survey every single member of a population because of fiscal restraints (or time restraints). For example, let’s say you wanted to know how many people in the USA were vegetarians. Then think about how long it would take you to call over 300 million people (assuming they all had phones and could speak!). The problems associated with surveying entire populations are why researchers survey just a fraction of the population — called a sample.
The problem with taking a sample of the population concerns the size of your sample. Obviously, if you asked one person in the population if they were vegetarian then their answer wouldn’t be representative of the entire population. But would 100 people be sufficient? 1000? Ten thousand? How you figure out a sufficient sample size involves applying a formula. While there are several formulas to calculate reasonable sample sizes, most of them require you to know something about the population, like the mean or standard deviation. But what if you knew nothing about your population? That’s where Slovin’s formula comes in.
When Slovin’s formula is used
If you have no idea about a population’s behavior, use Slovin’s formula to determine the sample size. Slovin’s formula (sometimes written as Sloven’s formula) was formulated by Slovin in 1960.
Slovin’s formula is:
n = N / (1 + Ne2)
n = Number of samples
N = Total population
e = Error tolerance
The error tolerance, e, can be given to you (for example, in a question), or if you’re a researcher you might want to figure out your own margin of error, just subtract your confidence level from 1. For example, if you wanted to be 98 percent confident that your data was going to be reflective of the entire population then”
1-0.98 = 0.02.