# Hypothesis Testing Type 1 Error (False Positive): This occurs when the model incorrectly rejects a true null hypothesis (H0 is true, but the model predicts H1). Type 2 Error (False Negative): This happens when the model fails to reject a false null hypothesis (H0 is false, but the model predicts H0). ## Smoke Alarm Example **Ho : There is no fire**\ **Ha : There is fire** Type 1 Error (False Positive): Think of it as a "False Alarm." Imagine a smoke detector in your house. A Type 1 error would be when the smoke detector goes off (alarm sounds) even though there is no actual fire. In this case, you falsely believe there is a problem when there isn't. Type 2 Error (False Negative): Think of it as a "Missed Opportunity." Continuing with the smoke detector example, a Type 2 error would be when the smoke detector fails to sound the alarm even though there is a real fire. In this case, you miss detecting the actual problem. These real-world scenarios can help you associate Type 1 errors with false alarms and Type 2 errors with missed opportunities. Remembering them in this context might make it easier to recall which is which. ## Judge and Defendant Example **Ho: Defendant is Innocent**\ **Ha: Defendant is Guilty** Type 1 Error : Defendant is pronounced guilty, when in reality he is not guilty. In this case Ho was actually true but Judge said Ho is false. Type 2 Error :Defendant is pronounced innocent, when in reality he was guilty. In this case Ho was actually false but Judge said Ho is true. ## Pregnancy Example **Ho: Patient not pregnant**\ **Ha: Patient pregnant** Type 1 Error : Telling a man that he is pregnant\ Type 2 Error : Telling a pregnant woman that she is not pregnant ## Simple Explanation Type 1 error occurs when model says that Ho is false and Ha is true when in reality Ho is true and Ha is false. Type 2 error occurs when model says that Ho is true and Ha is false when in reality Ho is false and Ha is true. ### Images to understand {% embed url="" %} ### Precision vs Recall Mindset A system with high precision might leave some good items out, but what it returns is of high quality A system with high recall might give you a lot of duds, but it also returns most of the good items Precision: Book recommended out of all good books will be best book Recall: Burgular robing a house will take everything including useless things --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://gautamnaik1994.gitbook.io/snippets/data-science/statistics/hypothesis-testing.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.