Understanding Type-I and Type-II errors in hypothesis testing

We all can relate to thinking about whether route A will take less time than route B, if the average return on investment X is more than investment Y, and if movie ABC is better than movie XYZ. In all these cases, we are testing some hypotheses we have in our minds. Setting up hypotheses, proving/disproving them using data, and helping businesses make decisions is like bread and butter for Data Scientists. Data Scientists often rely on probabilities to understand the likelihood of observing data by chance and use that to make conclusions around a hypothesis. Hence, there are always scenarios of making errors while making conclusions around our assumed hypothesis. The below post is written to provide an intuitive yet detailed explanation of Type-I and Type-II errors that happen during statistical hypothesis testing.