Sometimes, however, your data fails to meet the second and/or third requirement. That the source population(s) can be reasonably supposed to have a normal distribution.That the scale of measurement for both samples has the properties of an equal interval scale.That the two samples are independently and randomly drawn from the source population(s).It does not store any personal data.As it was stated in Two sample t-Test, you can apply the t-test if the following assumptions are met: The cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. The cookie is used to store the user consent for the cookies in the category "Performance". This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Other. The cookies is used to store the user consent for the cookies in the category "Necessary". The cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional". The cookie is used to store the user consent for the cookies in the category "Analytics". These cookies ensure basic functionalities and security features of the website, anonymously. Necessary cookies are absolutely essential for the website to function properly. A very small P-value means that such an extreme observed outcome would be very unlikely under the null hypothesis.įor instance, if the appropriate (left-tail or right-tail) P-value found is higher than conventional criteria for statistical significance (0.001-0.05), we usually do not reject the null hypothesis and assume that all the differences between observed result and expected value are due to chance.įor a given test statistic \(z\) in the case of Standard Normal Distribution, the right-tail P-value is defined as: Simply speaking, the P-value is the probability of obtaining test results at least as extreme as the results actually observed, under the assumption that the null hypothesis is correct.
The P-value (probability value) is a quantitative parameter used in statistical hypothesis testing to determine whether a null hypothesis (or claimed hypothesis) is true, or in other words, whether the obtained test results are significant.
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