A null hypothesis is the first form of hypothesis testing conducted. They rely on rejecting the null hypothesis to prove the hypothesis. They readily allow you to determine if your data falls ...

Understanding the difference between a null hypothesis and a hypothesis can make or break your testing and analysis stages.

At the beginning of your experiment, you decided that if the probability was less than 0.01, you would reject your null hypothesis because the deviation would be significant and not due to chance.

In hypothesis testing, an analyst tests a statistical sample, intending to provide evidence on the plausibility of the null hypothesis. Statistical analysts measure and examine a random sample of ...

And if you end up refuting the null hypothesis -- that the business cannot possibly work -- you can rest assured that your chances for success have grown significantly higher.

A p-value of 0.001 indicates that if the null hypothesis tested were indeed true, then there would be a one-in-1,000 chance of observing results at least as extreme.

Known formally as null hypothesis significance testing, the practice assumes a null hypothesis (no difference, or no correlation, between experimental groups on measures of interest) and then ...

In his popular series, Andrew Vickers, PhD, discusses the null hypothesis, using surgical infection rates to demonstrate a problem in interpretation.

Climate change and the null hypothesis An excellent post by my colleague John Nielsen-Gammon, the Texas State climatologist, can be found here.An excerpt: ...