P Value Calculator with Chart

P-Value Calculator

Distribution:

Tails:

Significance level (α):

Z Score:

Mean (μ):

Standard deviation (σ):

DF :

DF_denominator :

Decimals:

Solution

Welcome to the P Value Calculator with Chart, a tool designed to help you compute p-values for different distributions and visualize the results. With this calculator, you can select from various distributions (Normal, T, Chi-Square, F), define the number of tails for your test, and specify parameters such as the significance level, mean, standard deviation, and degrees of freedom. Once you provide the required inputs, simply click on Calculate to obtain the p-value and a corresponding chart displaying your results.

Table of Contents

1 P Value Calculator
2 How to Use the P Value Calculator
3 What is the p-value?
4 Interpretation of the p-value
5 Domain of the p-value
6 How is the p-value calculated?

How to Use the P Value Calculator

Select a distribution: Choose the distribution for your hypothesis test (Normal, T, Chi-Square, or F).
Choose the number of tails: Specify whether your test is two-tailed, left-tailed, or right-tailed.
Set the significance level (α): Enter a value between 0 and 1 (default is 0.05).
Enter the test statistic: Depending on the selected distribution, provide the Z score, mean, standard deviation, or degrees of freedom as required.
Select decimal precision: Choose how many decimal places you want in the result.
Click Calculate: The calculator will compute the p-value and display it along with a chart summarizing the results.

This tool helps you quickly validate statistical hypotheses and visualize results in a clear and concise way. Please ensure that you input accurate parameters for the most reliable output.

What is the p-value?

The p-value, also known as the p-value, is a key measure in statistics. It represents the minimum probability required to reject the null hypothesis (H₀) given a distribution function and a test statistic.

Simply put, the p-value indicates how compatible the observed result is with the null hypothesis. The smaller the p-value, the less likely it is that the result occurred by chance.

Interpretation of the p-value

The p-value is interpreted as the area under the curve of the distribution function. If this value is very small, it implies that the test statistic is too extreme for the null hypothesis to be true. For example, a p-value of 0.03 indicates there is a 3% probability of obtaining such an extreme result if the null hypothesis is true.

Domain of the p-value

As a probability, the p-value always lies between 0 and 1. A value close to 0 suggests that the null hypothesis is unlikely to be true, while higher values support the null hypothesis.

How is the p-value calculated?

Although it is possible to calculate the p-value manually, it would require highly precise distribution tables. Fortunately, most statistical software performs this calculation automatically, making it easier to use in data analysis.

Decision Rule

To decide on the null hypothesis:

If p-value < significance level (e.g., 0.05): Reject the null hypothesis.
If p-value > significance level: Do not reject the null hypothesis.

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