Probability value:
P-value is a key concept in hypothesis testing. It shows the probability of getting a result that is equally likely as or more likely than the observed data. This statistic, which effectively quantifies the likelihood that an event will occur, is crucial in evaluating the degree of marginal significance in hypothesis testing.
Hypothesis Testing:
In the context of hypothesis testing, we frequently use data analysis and visualization to glean insights from sample datasets. In this procedure, the P-value approach becomes crucial since it assesses the importance of a given Null Hypothesis. Depending on a predefined significance level, this hypothesis will either be accepted or rejected.
Linear Model:
One or more independent variables and a dependent variable are assumed to have a linear relationship in a statistical procedure known as a linear model. It is used for modeling and forecasting, and common applications include simple linear regression (with a single predictor) and multiple linear regression (with several predictors)
Monte Carlo Test:
A statistical method for testing hypotheses and determining uncertainty is the Monte Carlo test. To approximate the distribution of a statistic or test statistic under the null hypothesis, it entails simulating a large number of random scenarios or samples. Researchers can determine a p-value, which denotes the likelihood of arriving at the observed result by chance, by comparing the observed statistic to the distribution of simulated values.