What information does a p value yield?
A p-value is a measure of the probability that an observed difference could have occurred just by random chance. The lower the p-value, the greater the statistical significance of the observed difference. P-value can be used as an alternative to or in addition to pre-selected confidence levels for hypothesis testing.
What does p-value tell?
The p-value, or probability value, tells you how likely it is that your data could have occurred under the null hypothesis. The p-value tells you how often you would expect to see a test statistic as extreme or more extreme than the one calculated by your statistical test if the null hypothesis of that test was true.
How do we interpret the p-value for this test?
The smaller the p-value, the stronger the evidence that you should reject the null hypothesis.
- A p-value less than 0.05 (typically ≤ 0.05) is statistically significant.
- A p-value higher than 0.05 (> 0.05) is not statistically significant and indicates strong evidence for the null hypothesis.
What do you mean by Bayesian p value?
1 Answer 1. If I understand it correctly, then a Bayesian p-value is the comparison of a some metric calculated from your observed data with the same metric calculated from your simulated data (being generated with parameters drawn from the posterior distribution).
Which is an important part of Bayesian inference?
An important part of bayesian inference is the establishment of parameters and models. Models are the mathematical formulation of the observed events. Parameters are the factors in the models affecting the observed data. For example, in tossing a coin, fairness of coin may be defined as the parameter of coin denoted by θ.
How does the Bayesian framework work in realtime?
In fact, the Bayesian framework allows you to update your beliefs iteratively in realtime as data comes in. It works as follows: you have a prior belief about something (e.g. the value of a parameter) and then you receive some data. You can update your beliefs by calculating the posterior distribution like we did above.
How are p-values measured with fixed size statistic?
1. p-values measured against a sample (fixed size) statistic with some stopping intention changes with change in intention and sample size. i.e If two persons work on the same data and have different stopping intention, they may get two different p- values for the same data, which is undesirable.