Statistical significance is a crucial concept for any scientist, market researcher, or pollster looking to ensure they’re making sound decisions with high-quality data. Read through this three-part guide from the experts at CloudResearch, and you’ll learn the underlying logic of statistical significance, the variables needed to calculate it, and a step-by-step guide to testing the results of your research.
The world today is drowning in data.
That may sound like hyperbole but consider this. In 2018, humans around the world produced more than 2.5 quintillion bytes of data—each day. According to some estimates, every minute people conduct almost 4.5 million Google searches, post 511,200 tweets, watch 4.5 million YouTube videos, swipe 1.4 million times on Tinder, and order 8,683 meals from GrubHub. These numbers—and the world’s total data—are expected to continue growing exponentially in the coming years.
What does it mean for research findings to be “statistically significant? ”
Simply stated, statistical significance is a way for researchers to quantify how likely it is that their results are due to chance. Statistically significant findings are those in which the researcher has confidence the results are real and reliable because the odds of obtaining the results just by chance are low.
Statistical significance testing involves several abstract concepts. So, we try to make things concrete with an example of how you might conduct a test of statistical significance.
In the U.S., there is a Presidential election every four years. In election years, there is a steady stream of polls in the months leading up to the election announcing which candidates are up and which are down in the horse race of popular opinion.
If you have ever wondered what makes these polls accurate and how each poll decides how many voters to talk to, then you have thought like a researcher who seeks to know how many participants they need in order to obtain statistically significant survey results.