P-Value Calculator
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Result
Your Result in Python
from scipy.stats import chi2
# Calculate the p-value Test
chi2_statistic =
# Specify the appropriate degrees of freedom
degrees_of_freedom =
# Calculate the right-tail probability
right_tail_prob =
# Calculate the left-tail probability
left_tail_prob =
# Calculate the two-tailed p-value by summing the probabilities
p_value =
# Print the p-value
print (paste ( p-value:' , p_value))
from scipy.stats import norm
# Calculate the p-value
z_score =
p_value =
# Print the p-value
print ( p-value:' ,p_value)
from scipy.stats import norm
# Calculate the p-value
t_statistic =
degrees_of_freedom =
p_value =
# Print the p-value
print ( p-value:' ,p_value)
Your Result in R
# Set the chi-square statistic
chi2_statistic <-
degrees_of_freedom <-
# Calculate the right-tail probability
right_tail_prob <-
# Calculate the left-tail probability
left_tail_prob <-
# Calculate the two-tailed p-value by summing the probabilities
p_value <-
# Print the p-value
print (paste ( p-value:' , p_value))
# Calculate the p-value for a
z_score <-
p_value <-
# Print the p-value
cat (' p-value:' , p_value, "\n")
# Calculate the p-value for a
t_statistic <-
degrees_of_freedom <-
p_value <-
# Print the p-value
cat (' p-value:' , p_value, "\n")
What Is the P Value? — Definition
IThe P-Value (also known as Probability Value) is a statistical tool that quantifies the importance of test findings in hypothesis testing. This concept informs statisticians or researchers of the likelihood that the observed outcomes would occur if the null hypothesis were correct. It allows researchers and statisticians to evaluate the strength of the evidence against the null hypothesis. Therefore, calculating P-Value is essential.
Formulae for Calculating P-Values
The formula used to determine P value varies according to the type of statistical test being performed. Therefore, in order to completely understand the concept of P-Value, it is essential to comprehend the associated terminologies.
Z Score
The Z-score calculates how much a data point deviates from a normal distribution’s mean by using the number of standard deviations. It is calculated according to the following formula:
Z=(X- μ)/σ
In the above expression, X represents the observed value or raw score, μ is the mean, and σ refers to the standard deviation.
T Score
If the population standard deviation is unknown or the sample size is small (under 30), T-score is used instead of the Z-score. Its computation is conducted through the following equation:
T =(Xˉ-μ)/s√n
In the above formula, X is the sample mean, μ represents the population mean, s refers to the sample standard deviation, and n is the sample size.
F Statistic
The F statistic is useful when comparing the variance of two or more samples. Therefore, it is used in ANOVA (ANalysis Of VAriance) tests. Its value is computed by dividing the variance of two samples with each other.
Correlation Coefficient (r)
When measuring the degree of the linear link between two variables and its direction, correlation coefficient (r) comes in handy. Its value has a range of -1 to 1, with -1 denoting perfect negative correlation, 0 representing no correlation, and 1 signifying perfect positive correlation.
Chi-Square
In order to determine whether a significant correlation between two category variables exists or not, P value is calculated from Chi-Square. This test proves valuable when computing the discrepancy between the actual and predicted frequencies in one or more categories. Its calculation involves summing an expression’s output in order to compare the values that are observed (O) and predicted (E). Here is a visual representation of its formula:
X2= (O-E)2/E
Examples Showcasing the Computation of P-Value
To fully grasp the concept of P-Value and its calculation, let’s consider two examples here.
Examples 1
If the P-Value is 0.0219 and the level of significance is 5%, check whether we can reject the null hypothesis or not.
Solution
To solve this, we’ll consider the description and hypothesis interpretation of P-values’ table. The table states that if P-Value is lower than the 0.05 level of significance, the null hypothesis can be rejected.
Examples 2
Suppose that the P-Value is 0.3105 and the level of significance is 5%. Determine whether can we reject the null hypothesis in such a situation?.
Solution
The P-Value in this example is greater than the 0.05 level of significance. Therefore, according to the P-Value table, we can’t reject the null hypothesis here.
Introducing Our P-Value Calculator — Step-by-Step Guide
P-Value discussions often involve the use of numerous related terminologies. Therefore, its concept seems complicated. To ease out this situation, we have developed an automated and reliable P-Value calculator. Our tool helps users find P values in an intuitive manner. Here is a general overview of using our P Value calculator:
Begin by picking the right statistical test based on your study design, such as Z-Score, T-Score, F Statistic, Correlation Coefficient (r), or Chi-Square.
Enter the parameters required for the chosen test, such as sample means, standard deviations, sample sizes, or correlation coefficients.
Tap on the ‘Compute P’ button to instantly begin calculation of the P-Value according to the provided data.
Our P-Value calculator will instantly complete the computation. Review the results the context of your hypothesis test to make better informed conclusions.
Hint: A high P-value denotes poor evidence, whereas a low P-value (usually < 0.05) shows significant evidence against the null hypothesis.
