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Use this P value calculator to calculate either one or two-tailed P values from statistical scores such as Z score, T score, F ratio score, Pearson (R) score, chi-square value, and Tukey Q score. With that, it automatically determines whether your results are statistically significant or not based on your chosen significance level.

**“The P-value is the probability of obtaining results at least as extreme as the observed ones, assuming the null hypothesis is true”**

When there is **no difference** between the **observed value** and the **expected value**, then this condition is known as the null hypothesis.

This condition shows a difference between the expected and the observed value. Meanwhile, it proposed that there is an **effect** on the data.

It means your results are so **unlikely to happen** randomly.

There are different statistical tests(Z score, T score, Chi-square, etc) and each test requires different parameters to calculate the p-value. A p-value is based on the probability distribution of the test under the null hypothesis (H₀).

A z score tells you how far a specific point is from the average(mean) value. The Z score depends on the **standard normal distribution**. It is used to find the difference for both large and small samples until the data follows the normal distribution.

Z = X- µ σ

How To calculate P Value From Z Score?

- Calculate the z-statistic for deriving the P-value with the given formula
- After getting the calculated test statistic
- Use the z-score table to determine the P-value

A t score, like a z score, is a standardized score used in statistics to know the distance of a point from the mean value. It is expressed in terms of standard deviation. Read on to know how to find p value from t score!

t = X- µ S ÷ n

P value can be easily determind by searching for the t score in a t-Distribution table or by putting it in a p value calculator from t score.** **

**Positive T-score:**The data point's value is**above**the mean value. It means the higher the p score, the above the data point is**Negative T-score:**It means that the data point is**below**the mean value**T-score of 0:**It means that the data point is**equal**to the mean

A chi-square test is used to determine the relationship between the categorical variables. With the help of the chi-square test, you can determine whether there’s a statistically significant difference or not between what you expected and what you observed in your data, especially when analyzing **surveys** with categorized answers. It helps you understand how likely the results are **due to chance. **

If the value of the difference is **large**, then it suggests a **relationship** between the variables. It does not provide any information about the **direction**(positive or negative). You can calculate P value from chi square.

X^{2} = Σ (O- E)^{2} E

The f statistic is used in conjunction with an F-test to assess the difference between variances of two or more groups (populations or samples). The interpretation of the F text depends upon the resulting p-value. Therefore stay attentive and focused whether you are performing the manual calculation or doing it with the help of the P-value calculator. If the value of p is low, then it means that the variance is likely **different**. While a greater p score indicates that the **null hypothesis** of variance can not be rejected.

F = (s_{1})^{2} (s_{2})^{2}

The degrees of freedom in the nominator is df_{1} = n_{1} - 1 and the degree of freedom for denominator is df_{2} = n_{2} - 1

Where:

- (s
_{1})^{2}indicates the first sample variance - (s
_{2})^{2}represents the second sample variance

Pearson (r) score is a statistical measure that finds the degree of **linear relationship** between two quantitive variables. It gives the value between -1 and +1, indicating the **relationship** and **direction**. You can use the number between -1 and +1 and the degree of freedom (N-2) to find the P value from the r score.

**Strong Positive Correlation:**If it gives a value that’s near the +1, then it means on increasing one value, the other value will also**increase****Strong Negative Correlation:**When the value is closer to the -1, then it means that on increasing one value the other value will**decrease****Weak or No Linear Correlation:**If the value is near 0, then it indicates a**weak**or**no relation**between the variables

Finding P value from the Pearson (r) score involves the following steps:

**Step #1:** Calculate the test statistic (t)

t= r n-2 (1 - r^{2})

**Step #2:** Determine the degrees of freedom (df) = n−2

**Step #3:** Use the t-distribution table to determine the critical t-value and interpolate (if necessary)

y = y + (x - x_{1})(y_{2} - y_{1}) x_{2} - x_{1}

**Step #4: **Approximate P value

Use a P value table/chart to approximate the P value or get the exact P value effortlessly by using our P value calculator.

Tukey's HSD (Honestly Significant Difference) is the test that compares groups in the data and finds significant differences to determine whether they are significant or not.

To find p value from Tukey q Score:

**Determine the Tukey Q Score:**It shows the**magnitude**of the difference between two groups**Calculate the Degrees of Freedom(df):**It relies upon the**number of groups**that are compared and the**sample size****Studentized Range Distribution Table:**Use the table that contains the calculated**q-score**and**degrees of freedom****Approximate the interpolation:**If the q score does not match with any value of the table then use the**interpolation**to find p value

While we mentioned how easily you can calculate p-values from various statistics. For more convenient calculations, you can start using our P value calculator. It uses different scores and appropriate distribution to provide you P-value directly.

- When the P-value is
**lower**than the significance, then it means that the result is**statically significant** - When the P-value is
**higher**than the significance, then it indicates that the result is**not significant**and the p-value does not provide the evidence to**reject**the**null hypothesis**

**References: **

From the source of Wikipedia.org: P-Value.

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