Hey! I have been reading about Type 1 error and Type 2 error, but the definitions I find contain numerous technical terms which make it unable for me to understand the core concept of these errors. I am completely new to this field and have little knowledge of the technical terms. I would be happy to find a simple definition of the Type 1 error and Type 2 error.
Hello! I can feel your frustration. I went through an identical crisis when I started my journey in statistical hypothesis. Here, I have tried to define the Type 1 error and Type 2 error as simply as I can frame it.
Type 1 error, also known as false positive, occurs when you reach to the wrong assumption that your hypothesis testing worked, even if it did not. This error arises when the test duration and the sample size is mishandled or neglected.
Type 2 error, also referred to as false negative, occurs when the researcher refuses to reject a null hypothesis, which in reality is false. He concludes that there is no important impact when, actually, there is.
