## P-value in statistics: understanding the p-value and what it

The p-value is a simpler representation of the likelihood value. This is the calculated value obtained from hypothesis experiments in order to determine if one or more classes vary statistically.
A null hypothesis and an alternative hypothesis are used in hypothesis experiments. The null hypothesis states that all groups are equal, while the alternative hypothesis states whether at least one group is greater than, less than, or not equal to the others.
If the p-value is greater than 0.05, you can’t rule out the possibility that the data classes are identical (fail to reject the null hypothesis). That either means they’re identical or came from the same original source (distribution), or you don’t have enough data to statistically confirm any discrepancies.
While not all decisions are based on 0.05, it is a commonly used and agreed cut-off point in most scientific research and academic articles. In other words, if you decide to dismiss the null hypothesis with a p-value of 0.04, there’s a 4% chance you’re wrong. In certain cases, taking less than 5% risk is considered appropriate, but for some decisions, this might be too risky or too cautious.

## P-values and significance tests | ap statistics | khan academy

a brief introduction

### What to do when a p-value of .000 is reported

You will find out how to conduct a hypothesis test by determining the form of distribution, sample size, and known or unknown standard deviation. When devising a hypothesis test, however, there are many other considerations to consider.
The following is a summary of the hypothesis testing thought process: You want to see if a certain population property is valid. For numerical data, you make an assumption about the true population mean, and for categorical data, you make an assumption about the true population proportion. The null hypothesis is based on this premise. Then you collect data from a representative sample of the population. You calculate the sample mean using this sample data (or the sample proportion). If the null hypothesis is valid, and the value you find is very unlikely to occur (a unusual event), you can wonder why this is happening. The null hypothesis is incorrect, according to one possible interpretation.
You can calculate the likelihood of obtaining the test statistic (the sample mean, sample proportion, or other test statistic) when the null hypothesis is accurate after you collect data and obtain the test statistic (the sample mean, sample proportion, or other test statistic). The p-value refers to this likelihood.

### Week 5 : tutorial: hypothesis testing in stata

Trying to remember what the alpha-level, p-value, and confidence interval for a hypothesis test all mean—and how they relate to one another—can be as difficult as Dorothy’s journey down the yellow brick road.
You must first decide your alpha level, also known as the “significance level,” before running any statistical test. The alpha amount, by definition, is the likelihood of rejecting the null hypothesis when it is valid.
Most people use an alpha level of 0.05, thanks to renowned statistician R. A. Fisher. However, if you’re looking at airplane engine failures, you may want to use a smaller alpha to reduce the chances of making a bad decision. If you’re making paper airplanes, on the other hand, you may be able to raise alpha and consider a higher chance of making a bad decision.
The p-value, in statistical terms, is the likelihood of having a result as extreme as, or more extreme than, the actual result when the null hypothesis is true. If that makes your head spin like Dorothy’s house in a Kansas storm, just imagine Glenda has zapped it from your brain with her magic wand. Then think about it for a moment.

### P value | hypothesis testing

You’re filling in the blank space in the previous sentence when you pick a significance level. You’re determining how unlikely a coincidence has to be before you’ll consider the idea that something is going on.
This is a brilliant query. The bottom line is that the.05 and.10 cutoffs are arbitrary and are decided by tradition and common experience. They’re simply metrics that show when we can be confident in rejecting the null hypothesis that the coefficient or test statistic is not zero, implying that there is most likely an impact, difference, or whatever you’re measuring. In the sense that the cutoffs have no natural value, they are arbitrary. It’s absurd to call a p-value of.49 “important” when a p-value of.51 is “insignificant.” At the.05 mark, there is no magical transformation that occurs.