Looking at the growth rate of the SaaS industry, you’d think that people are more than willing to spend money on software solutions that promise to make their everyday lives (and professional endeavors) easier. Not quite, though. Despite projections forecasting the total SaaS spend to reach $171.9 billion in 2022, most buyers still prefer free solutions. […]
What is Statistical Significance?
Statistical significance is the likelihood that a relationship between two or more variables is caused by something more than just random chance. In other words, it is the likelihood that the difference in conversion rates between any variation and the baseline is not a random occurrence.
When testing a hypothesis, the result of an experiment is thought to have statistical significance or to be statistically significant, meaning results are likely not caused by chance for any given level of statistical significance. When you’ve found the statistical significance level, it reflects the risk tolerance and confidence level of your hypothesis.
For example, say you run a split-testing experiment and find a significance level of 95%. This means that if you determine a winner within the split test, you can be 95% confident that the results you have observed are actually real and not an error caused by random chance.
A 95% confidence level means there is a 5% chance you could be wrong about your results. Thus, statistical significance allows you space to make better-informed decisions about marketing practices for your business.