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P-Value Calculator FullScreen

You can use this p-value calculator to calculate the right-tailed, left-tailed, or two-tailed p-values for a given z-score. It also generates a normal curve and shades in the area that represents the p-value.

P-Value from Z-Score Calculator


What is the formula to calculate p-value?

It is very difficult to calculate p-value manually. The most commonly employed way of doing this is to utilize a z-score table. In a z-table, the zone under the probability density function is presented for each value of the z-score.

It is also possible to employ an integral to determine the area under the curve. The standard normal distribution function that is used to do this is as follows:

φ(z) = (1 / √2π) × e -z2/2

Where: −∞ < z < ∞, e is the base of the natural logarithm (2.718282), π is the constant (3.1415926).

What is P-Value Calculator

A P-value calculator is a statistical tool used to determine the probability (p-value) of obtaining observed data or more extreme results under the null hypothesis of a statistical test. The p-value helps evaluate the strength of evidence against the null hypothesis and assess the statistical significance of the test results.

To use a P-value calculator, you typically follow these steps:

  1. Select a Statistical Test: Identify the appropriate statistical test based on your research question and the type of data you have. Examples include t-test, chi-square test, ANOVA, correlation, regression, etc.

  2. Define Null and Alternative Hypotheses: Formulate the null hypothesis (H0), which assumes no effect or no difference between groups, and the alternative hypothesis (Ha), which suggests there is an effect or a difference.

  3. Gather Data: Collect the relevant data required for your chosen statistical test from your study or experiment.

  4. Choose Significance Level (Alpha): Determine the significance level (alpha) at which you want to evaluate the test results. Commonly used values are 0.05 (5%) and 0.01 (1%).

  5. Perform Statistical Test: Use appropriate software or statistical tools to perform the selected statistical test using the collected data. This will provide you with test statistics and degrees of freedom.

  6. Use P-Value Calculator: Input the obtained test statistics and degrees of freedom into a P-value calculator. The calculator will determine the p-value associated with the observed test statistic under the null hypothesis.

  7. Interpret Results: Compare the obtained p-value with the chosen significance level (alpha). If the p-value is less than or equal to alpha, the result is considered statistically significant, and you reject the null hypothesis. Otherwise, if the p-value is greater than alpha, the result is not statistically significant, and you fail to reject the null hypothesis.

The p-value represents the probability of observing the data or more extreme results assuming that the null hypothesis is true. A smaller p-value indicates stronger evidence against the null hypothesis, suggesting that the observed results are unlikely to occur by chance alone.

P-value calculators make it convenient to obtain p-values without manually calculating them using probability distributions. They can be found online or within statistical software packages, and they simplify the process of evaluating statistical significance.

Remember that interpreting p-values requires caution, and they should not be considered as measures of effect size or practical significance. The significance level (alpha) should be chosen based on the specific context and research field.

If you have specific data and hypotheses, I can assist you further by performing the statistical test and calculating the p-value for you.

P-Value Calculator Example

Sure! Here's an example of how to calculate the p-value for a statistical test:

Let's say we want to calculate the p-value for a two-sample t-test. We have two groups, Group 1 and Group 2, and we want to compare their means.

Group 1: [5, 8, 7, 6, 10] Group 2: [12, 9, 11, 13, 8]

Step 1: Perform the t-test to calculate the t-statistic.

Assuming equal variances, we can use the independent samples t-test. The t-statistic is calculated as:

t = (x̄1 - x̄2) / sqrt((s1^2 / n1) + (s2^2 / n2))

where x̄1 and x̄2 are the sample means, s1 and s2 are the sample standard deviations, and n1 and n2 are the sample sizes.

For Group 1:

  • x̄1 = (5 + 8 + 7 + 6 + 10) / 5 = 7.2
  • s1 = sqrt(((5 - 7.2)^2 + (8 - 7.2)^2 + (7 - 7.2)^2 + (6 - 7.2)^2 + (10 - 7.2)^2) / (5-1)) ≈ 1.923

For Group 2:

  • x̄2 = (12 + 9 + 11 + 13 + 8) / 5 = 10.6
  • s2 = sqrt(((12 - 10.6)^2 + (9 - 10.6)^2 + (11 - 10.6)^2 + (13 - 10.6)^2 + (8 - 10.6)^2) / (5-1)) ≈ 2.588

Plugging these values into the t-statistic formula: t = (7.2 - 10.6) / sqrt((1.923^2 / 5) + (2.588^2 / 5)) ≈ -2.038

Step 2: Determine the degrees of freedom.

For the independent samples t-test, the degrees of freedom are calculated using the following equation:

df = (s1^2 / n1 + s2^2 / n2)^2 / ((s1^2 / n1)^2 / (n1 - 1) + (s2^2 / n2)^2 / (n2 - 1))

Plugging in the values: df = (1.923^2 / 5 + 2.588^2 / 5)^2 / ((1.923^2 / 5)^2 / (5 - 1) + (2.588^2 / 5)^2 / (5 - 1)) ≈ 7.74

Step 3: Calculate the p-value.

To calculate the p-value, we need to determine the probability of observing a t-value as extreme as the one calculated (or more extreme) under the null hypothesis.

Using statistical software or a t-table, we can find the p-value associated with the t-value and degrees of freedom. Let's assume we find that the p-value is 0.025.

Step 4: Interpret the results.

If the p-value is less than the chosen significance level (e.g., α = 0.05), we reject the null hypothesis. In this example, since the p-value (0.025) is less than 0.05, we would reject the null hypothesis.

Remember, this is just an example of a two-sample t-test. The specific steps and formulas may vary depending on the statistical test you are performing and the software or programming environment you are using.