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The Null Hypothesis For Popper, a scientific method is "proposing bold hypotheses, and exposing them to the severest criticism, in order to detect where we have erred." (Popper, 1974, p.68) If the hypothesis can stand "the trial of fire," then we can confirm its validity” (Yu, 2012).

A Null Hypothesis Example: The null hypothesis states that “there is no association between the predictor and outcome variables in the population” (Banerjee, et.

which we get by inserting the hypothesized value of the population mean difference (0) for the population_quantity. If or (that is, ), we say the data are not consistent with a population mean difference of 0 (because does not have the sort of value we expect to see when the population value is 0) or "we **reject the hypothesis that the population mean difference is 0**". If t were -3.7 or 2.6, we would reject the hypothesis that the population mean difference is 0 because we've observed a value of t that is unusual if the hypothesis were true.

If (that is, ), we say the data are consistent with a population mean difference of 0 (because has the sort of value we expect to see when the population value is 0) or "we **fail to reject the hypothesis that the population mean difference is 0**". For example, if t were 0.76, we would fail reject the hypothesis that the population mean difference is 0 because we've observed a value of t that is unremarkable if the hypothesis were true.

If you fail to see effects of variations in your alternative hypotheses where they actually exist, you may be wasting your time and not taking advantage of opportunities to improve your .

Let’s consider a hypothetical situation. You are in charge of an ecommerce site and you are testing variations of a landing page. We’ll examine how a type 2 error could negatively your company’s revenue.

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Statisticians speak of two significant sorts of statistical error. The context is that there is a "null hypothesis" which corresponds to a presumed default "state of nature", e.g., that an individual is free of disease, that an accused is innocent, or that a potential login candidate is not authorized. Corresponding to the null hypothesis is an "alternative hypothesis" which corresponds to the opposite situation, that is, that the individual has the disease, that the accused is guilty, or that the login candidate is an authorized user. The goal is to determine accurately if the null hypothesis can be discarded in favor of the alternative. A test of some sort is conducted (a blood test, a legal trial, a login attempt), and data is obtained. The result of the test may be negative (that is, it does not indicate disease, guilt, or authorized identity). On the other hand, it may be positive (that is, it may indicate disease, guilt, or identity). If the result of the test does not correspond with the actual state of nature, then an error has occurred, but if the result of the test corresponds with the actual state of nature, then a correct decision has been made. There are two kinds of error, classified as "Type I error" and "Type II error," depending upon which hypothesis has incorrectly been identified as the true state of nature.

Your hypothesis test involves changing the “Buy Now” from green to red will significantly increase conversions compared to your original landing page. You launch your A/B test and wait for the random sample of data to trickle in.

A Type I error (sometimes called a Type **1** error), is the **incorrect rejection of a true null hypothesis**. The alpha symbol, α, is usually used to denote a Type I error.

The null hypothesis, H_{0} is a commonly accepted hypothesis; it is the opposite of the . Researchers come up with an alternate hypothesis, one that they think explains a phenomenon, and then work to . If that sounds a little convoluted, an example might help. Back in the day (way back!) scientists thought that the Earth was at the center of the Universe. That mean everything else — the sun, the planets, the whole shebang, all of those celestial bodies revolved around the Earth.

**Type II error**, also known as an "**error of the second kind**", a **β error**, or a "**false negative**": the error of failing to reject a null hypothesis when the alternative hypothesis is the true state of nature. In other words, this is the error of failing to observe a difference when in truth there is one. This type of error can only occur when the statistician fails to reject the null hypothesis.

While it is impossible to completely avoid type 2 errors, it is possible to reduce the chance that they will occur by increasing your sample size. This means running an experiment for longer and gathering more data. This will help avoid reaching the false conclusion that an experiment does not have any impact, when it actually does.

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