A higher value shows a greater significance. Higher and lower values of the p-value show the relative strength of significance. The p-value is a single calculation that allows you to determine whether any difference you’re seeing is due to random variation or true change. Relying on the p-value as a guide will help you decide what to do about your Ho. The p-value is an objective and statistical method for making proper decisions about your process. Why? Since you were only willing to be wrong 5% of the time when rejecting your Ho, and the p-value says that if you reject your Ho, you will be wrong 18% of the time, common sense says to not reject the null and go back to the drawing board. Why? Since you are willing to be wrong 5% of the time, but your p-value says you will only be wrong 1% of the time if you reject your Ho, then you will likely go for it and reject your Ho.īut if your p-value was 18%, stay in your own office. The rule of thumb is, if the p is low (less than your alpha risk), the Ho should be rejected, but if your p is high (greater than your alpha risk), don’t reject the Ho.įor example, if you chose an alpha risk of 5%, and your calculation of p-value was 1%, then you should march into your boss’s office and proclaim success. To help you determine whether you should go to your boss and declare victory, you will compare your chosen alpha risk with your actual calculated risk and decide what to do next. So, where does the p-value fit into all of this? The p-value is your calculated risk of making an alpha error, or claiming success when there really wasn’t any. That percent of risk is typically in the range of 1%, 5%, or 10%. This is thinking that your new marketing program actually increased sales when in fact, it didn’t. This is rejecting your Ho when you shouldn’t. In hypothesis testing, you can make an alpha error. The third step is your selection of alpha risk. In this example, your Ha would be written as mu1 – mu2 is not equal to zero. Your second step would be to state your alternate or alternative (Ha) hypothesis. In this case, you’re stating that there is an absence of difference (or no difference) between the two machines. For example, if you’re interested in the difference in the average run speed between two machines, you would write the Ho as mu1 – mu2 = 0, where mu represents the population average of the machine speeds for machine 1 and machine 2. That will always be in the form of the absence of the characteristic in question. 3 steps to the p-valueįirst, you must state your Ho. There are three initial steps you will take when doing a hypothesis test, regardless of the type of question that you are trying to answer. To determine the answers, you will want to use the statistical technique of hypothesis testing.
In a business setting, you’re often faced with a number of questions, such as: P-value is used in the context of hypothesis testing, specifically the null hypothesis (Ho).
#P value minitab express how to#
Overview: What is the p-value?īefore explaining what the p-value is all about, let’s provide a little context and background to help you better understand what it means and how to interpret it. We will provide you with some of the benefits for knowing and understanding the concept of p-value and some best practices for applying the technique. That is, just random variation or true difference. This article will explain how the p-value will help you decide whether what you are looking at is noise or signal. But, can they tell you whether that difference is because of random variation or whether it is a statistically significant difference? Probably not. Any third-grade student can tell you whether there is a mathematical difference between two values.
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