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In this case, the value is called the “p-value”. It is often used in the psychology world to compare two populations to see if the measurements from a particular experiment are statistically significant. What is a p-value? It is the probability that the difference in measurements would be due to chance, or so small that it is deemed negligible.
The p-value is calculated using the sampling distribution of the test statistic under the null hypothesis, the sample data, and the type of test being done (lower-tailed test, upper-tailed test, or two-sided test). (Source: support.minitab.com)
The P value is an important part of statistical significance. It is the probability of observing a result different from the null hypothesis when it is in fact the case. When calculating a p value, it is important to evaluate both the numerator and the denominator. For example, if you want to calculate a p value with a 10% significance, then the numerator would be 10% while the denominator would 100%.
Problem: Bob wants to know if the mean height of a certain species of plant is equal to 15 inches. To test this, he collects a random sample of 20 plants and finds that the sample mean is 14 inches and the sample standard deviation is 3 inches. Conduct a t-test using a .05 alpha level to determine if the true mean height for the population is actually 15 inches. (Source: www.statology.org)
Measures of variability are important for data in many statistical tests, but what exactly is a p value? A p value is a value that can be calculated by dividing the probability of observing data equal to or more extreme than what it actually is by the probability of observing data less extreme than what it actually is. This can be calculated using the p formula: p = 1 - . 5.