Please help me to understand the p-value concept in prospect of Multiple Regression (Backward Elimination). My understanding here is that

  1. The null hypothesis is we will assume that the dependent variable depends on all the independent variables.
  2. The alternate hypothesis is there is a significant chance that few or all of the independent variables has no significant impact on the value of the dependent variable

Now a significance value (alpha) means how much error (5%) we can tolerate and still say the null hypothesis is true whereas the p-value shares actual amount of error that our sample data is tolerating.

Suppose the case is P-value > alpha, so it means that our model is tolerating more than 5% error


Hence it clearly states that we have much more deviations from the null hypothesis and hence the alternate hypothesis is correct(i.e. if p-value is more we we rejecting the null hypothesis)

However from other sources i read that a null hypothesis should be rejected if the p-value is low.

Hence i am totally confused as what is happening. Please help.


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