{"id":249266,"date":"2024-06-21T18:39:17","date_gmt":"2024-06-21T18:39:17","guid":{"rendered":"https:\/\/namso-gen.co\/blog\/?p=249266"},"modified":"2024-06-21T18:39:17","modified_gmt":"2024-06-21T18:39:17","slug":"what-does-p-value-mean","status":"publish","type":"post","link":"https:\/\/namso-gen.co\/blog\/what-does-p-value-mean\/","title":{"rendered":"What does p-value mean?"},"content":{"rendered":"

The concept of p-value is widely used in statistics to measure the strength of evidence against the null hypothesis. It helps determine whether the results of a statistical analysis are statistically significant or simply due to chance. The p-value is a numerical value between 0 and 1, with smaller values indicating stronger evidence against the null hypothesis.<\/p>\n

The p-value is a statistical measure that quantifies the probability of observing a test statistic as extreme as, or even more extreme than, the one calculated from the sample data, assuming the null hypothesis is true.<\/b> In simpler terms, it tells us how likely we would obtain the observed data if the null hypothesis were true. <\/p>\n

The null hypothesis refers to the default assumption that there is no meaningful relationship or difference between variables or no effect of a treatment. The alternative hypothesis, on the other hand, states that there is indeed a significant relationship or difference.<\/p>\n

The p-value is compared to a predetermined significance level (usually denoted as \u03b1) to make a decision. If the p-value is smaller than the significance level (typically \u03b1 = 0.05), it is considered statistically significant. In such cases, the evidence suggests that the null hypothesis is unlikely to be true, and we reject it in favor of the alternative hypothesis. Conversely, if the p-value is larger than \u03b1, we fail to reject the null hypothesis.<\/p>\n

\n
\n

Table of Contents<\/p>\nToggle<\/span><\/path><\/svg><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n