Is your data sufficient to reject the null hypothesis? Calculate p-value with our calculator.
The p-value is
0
The result is not significant at p ≥
0.05
Picture this: you're diving deep into the world of analytics and statistics, trying to make sense of all those numbers and data points. Suddenly, you stumble upon a little gem called the p-value. It's like a secret code that researchers use to unlock the mysteries of hypothesis testing and significance.
The primary use of the p-value is for decision-making in hypothesis testing. It helps researchers assess if the observed data is enough to reject the null hypothesis for an alternative hypothesis. Researchers also use the p-value to compare groups or test for correlations.
Gather answers using the SurveyMonkey p-value calculator above.
The p-value stands for probability value. It measures the likelihood of a result, assuming the null hypothesis is true. It's a probability gauge showing how likely your result is, assuming no real difference (the null hypothesis).
The p-value quantifies the strength of evidence against the null hypothesis. It is typically compared to a predetermined level of significance, such as 0.05. When the p-value is low, it tells you, "This result probably didn't happen by chance!" This gives you the green light to reject the null hypothesis and consider that your hypothesis might be true.
The p-value is important because researchers use it to decide whether to accept or reject the null hypothesis. Some examples of research questions that can use the p-value are:
A low p-value suggests there are differences among the groups you tested. It also indicates that real, predictable relationships among variables may exist.
Researchers can then interpret the significance of their findings and communicate the strength of evidence to stakeholders and peers.
To calculate a p-value, first determine the probability of obtaining your data if the null hypothesis were true. Then, compare this probability to your chosen significance level (usually 0.05) to decide if your results are statistically significant.
To calculate a p-value from a z-score, look up the z-score in a standard normal distribution table. Alternatively, use software to find the corresponding probability. This probability represents the likelihood of observing a value as extreme as the z-score under the null hypothesis.
The following formulas give the p-value:
Here’s the step-by-step guide on how to calculate the p-value from a z-score:
To calculate a p-value from a t-score, first, determine the t-score representing the difference between your sample mean and the population mean. Then, use a t-distribution table or software to find the probability of observing that t-score. This indicates the likelihood of obtaining your sample results under the null hypothesis.
The following formula gives the p-value from the t-score.
Where cdft,d represents the cumulative distribution function of the t-Student distribution with d degrees of freedom.
Here’s the step-by-step guide on how to calculate the p-value from a t-score:
To obtain the p-value for a Pearson correlation coefficient, first use the calculated coefficient to derive a t-statistic. Then, you can find its associated p-value using the t-distribution with degrees of freedom (n - 2).
The formula to get the t-statistic from a Pearson correlation coefficient is below:
Where:
After obtaining the z-score, you can calculate the p-value using the cumulative distribution function of the t-distribution. This uses n - 2 degrees of freedom, where n is the sample size.
Here's the general process:
To calculate the p-value from a chi-square score, determine the degrees of freedom associated with the chi-square distribution. Then, use statistical tables or software to find the probability of obtaining a chi-square value as extreme as the observed one.
You can get the p-value with the help of the following formula:
p-value=1− cdfχ² (x; df)