In the last year, statistics have been unusually important in the news. How accurate is the COVID-19 test you or others are using? How do researchers know the effectiveness of new therapeutics for COVID-19 patients? How can television networks predict the election results long before all the ballots have been counted?

Each of these questions involves some uncertainty, but it is still possible to make accurate predictions as long as that uncertainty is understood. One tool statisticians use to quantify uncertainty is called the margin of error.
Limited data
I am a statistician, and part of my job is to make inferences and predictions. With unlimited time and money, I could simply test or survey the entire group of people I am interested in to evaluate the question in mind and find the exact answer. For example, to find out the COVID-19 infection rate in the U.S., I could simply test the entire U.S. population. However, in the real world, you can never access 100% of a population.

Instead, statisticians sample a small portion of the population and build a model to make a prediction. Using statistical theory, that result from the sample is extrapolated to represent the whole population.

Ideally, a good sample should be representative of the total population, including gender, racial diversity, socioeconomic diversity, lifestyle patterns and other demographic measures. The larger the sample, the more similar it would be to the true population, and with a larger sample, the more confident statisticians become in their predictions. But there will always be some uncertainty.