Introduction to Type I and Type II errors (video) | Khan Academy
The difference between a type II error and a type I error is a type I error rejects the null hypothesis when it is true. The probability of committing a. In the social media, a type of humorous meme that is prevalent is the “Photoshop .. (2-tailed) Mean Difference Std. Error Difference Shares Type I and Type II errors, β, α, p-values, power and effect sizes – the ritual of null hypothesis significance testing contains many strange concepts. Much has.
Usually a type I error leads one to conclude that a supposed effect or relationship exists when in fact it doesn't.
Examples of type I errors include a test that shows a patient to have a disease when in fact the patient does not have the disease, a fire alarm going on indicating a fire when in fact there is no fire, or an experiment indicating that a medical treatment should cure a disease when in fact it does not.
Examples of type II errors would be a blood test failing to detect the disease it was designed to detect, in a patient who really has the disease; a fire breaking out and the fire alarm does not ring; or a clinical trial of a medical treatment failing to show that the treatment works when really it does.
Thus a type I error is a false positive, and a type II error is a false negative. When comparing two means, concluding the means were different when in reality they were not different would be a Type I error; concluding the means were not different when in reality they were different would be a Type II error.
Top 10 Programmer Jokes, Explained for the Rest of Us
Various extensions have been suggested as " Type III errors ", though none have wide use. In practice, the difference between a false positive and false negative is usually not obvious, since all statistical hypothesis tests have a probability of making type I and type II errors.
These error rates are traded off against each other: For a given test, the only way to reduce both error rates is to increase the sample size, and this may not be feasible. A test statistic is robust if the Type I error rate is controlled. These terms are also used in a more general way by social scientists and others to refer to flaws in reasoning. Statistical test theory[ edit ] In statistical test theory, the notion of statistical error is an integral part of hypothesis testing.
The test requires an unambiguous statement of a null hypothesis, which usually corresponds to a default "state of nature", for example "this person is healthy", "this accused is not guilty" or "this product is not broken".
Top 10 Programmer Jokes, Explained for the Rest of Us | iD Tech
An alternative hypothesis is the negation of null hypothesis, for example, "this person is not healthy", "this accused is guilty" or "this product is broken". There are two ways to write error-free programs; only the third one works. This joke refers to the fact that it is actually impossible to write an error-free program. Since there is always one more bug, the joke says only the third, non-existent method is the only way to write an error-free program.
The best thing about a Boolean is even if you are wrong, you are only off by a bit.
10 memes only a programmer will understand
A Boolean is a data type which can only have one of two possible values: A data type just means what type of data is held within something like a variable. Variables in programming are similar to variables you might have seen in math class, with the difference being that a variable in programming can represent more than just a number.
Booleans are typically stored within a bit, which is the smallest amount of storage in a computer. It holds a single binary digit. Binary, being a base-2 number system, means it can only hold the value 0 or 1. In the case a Boolean, 0 usually means false while 1 is usually used for true.
The joke then, is that if you have a Boolean, the most you can be off is a bit, which would just be 0 or 1.
A good programmer is someone who always looks both ways before crossing a one-way street. They need to check that what they got is actually a number and not a word or symbol or was left blank. Removing the needles from the haystack.
Debugging is the process in which you remove bugs from your program. Since finding bugs and their causes can frequently be tricky, finding them is like finding a needle in a haystack. So, debugging is like removing the needles from a haystack your program.
Tags usually have an opening and closing tag. How do you annoy a web developer?
Mixing the two up is however not, and any good web developer would want to fix. The program looks like this: