Today I learnt about the concept of P-value which is the probability value to measure the chances of an original event (Null Hypothesis) to occur under the assumption that the null hypothesis is true. If the probability of occurrence of the event falls to a point where the null hypothesis seems insignificant (impossible to occur by chance), then the null hypothesis is rejected. This p value helps measure the statistical significance of the null hypothesis which helps predict the true nature (true or false) of the event. For example, say we have a hypothesis of a fair coin (null hypothesis) and with every toss of the coin we get the outcome as tails, then each time the outcome of the event drops the probability of occurrence and when we reach a significantly low p-value, we say that the chances of that event (tails) to occur is significantly low. Hence, we conclude that the null hypothesis is not true and therefore can be rejected (It is not a fair coin). In a similar way, we can predict the statistical significance of any hypothesis by finding its p-value and if the p value is too low then most probably the null hypothesis is false.
We also learnt about the Breusch-Pagan Test, which assumes the null hypothesis where the data is evenly distributed (homoscedastic). If the p value is significantly low (less than 0.5) then we conclude that the null hypothesis is false and the data is heteroscedastic. I plan on leaning to run the test on a smaller data set using python and then testing the same on our real data.