CandleFocus Glossary Editor
The P-Value is a vital concept in statistics, as it helps scientists or researchers quantify their findings. It is a measure that allows researchers to determine whether or not their results are significant and could be used to draw a conclusion.
In layman’s terms, P-Value is a way for the researcher to mathematically calculate the probability that their findings could have been the product of a random chance. A low P-Value can mean that there is substantial evidence to suggest that the hypothesis is true and the results were not caused by a random chance.
When conducting a hypothesis test, the researcher starts with a specific hypothesis, or a guess as to what the results may be. After collecting the data, the researcher checks the probability of the observed results given that the hypothesis is true. This probability is calculated using a statistic known as the P-Value. The lower the P-Value, the more likely it is that the hypothesis is true. Generally, a P-Value below 0.05 is considered statistically significant and is used to draw the conclusion that the hypothesis is true.
In some hypotheses tests, there are preselected confidence levels used to draw a conclusion. Instead, the P-Value serves as a measure that can be used to validate the results. For example, a P-Value of 0.0001 would indicate that the same results would not be obtained randomly and the hypothesis can be considered validated with a high degree of confidence.
In short, the P-Value is an important statistical measurement used to validate a hypothesis based on observed data. It is used to quantify the probability of obtaining the observed results, given that the hypothesis is true. Furthermore, it is a common measure used to evaluate the statistical significance of the results and can be used as an alternative—or in addition to—preselected confidence levels for hypothesis tests.
In layman’s terms, P-Value is a way for the researcher to mathematically calculate the probability that their findings could have been the product of a random chance. A low P-Value can mean that there is substantial evidence to suggest that the hypothesis is true and the results were not caused by a random chance.
When conducting a hypothesis test, the researcher starts with a specific hypothesis, or a guess as to what the results may be. After collecting the data, the researcher checks the probability of the observed results given that the hypothesis is true. This probability is calculated using a statistic known as the P-Value. The lower the P-Value, the more likely it is that the hypothesis is true. Generally, a P-Value below 0.05 is considered statistically significant and is used to draw the conclusion that the hypothesis is true.
In some hypotheses tests, there are preselected confidence levels used to draw a conclusion. Instead, the P-Value serves as a measure that can be used to validate the results. For example, a P-Value of 0.0001 would indicate that the same results would not be obtained randomly and the hypothesis can be considered validated with a high degree of confidence.
In short, the P-Value is an important statistical measurement used to validate a hypothesis based on observed data. It is used to quantify the probability of obtaining the observed results, given that the hypothesis is true. Furthermore, it is a common measure used to evaluate the statistical significance of the results and can be used as an alternative—or in addition to—preselected confidence levels for hypothesis tests.