high bias ( under fitting): fixed by trying a larger of features, 
high variance (overfitting): fixed by trying a smaller of features, including more training examples.
L1 norm
L2 norm
Likelihood ratio test: comparing the goodness of fit of two statistical models — a null model against an alternative model.  expresses how many times more likely the data are under one model than the other.
Standard error: estimated as the sample standard deviation divided by the square root of the sample size
P values
The p-value is defined as the probability, under the null hypothesis, of obtaining a result equal to or more extreme than what was actually observed.
The smaller the p-value, the higher the significance because it tells the investigator that the hypothesis under consideration may not adequately explain the observation. 
Type I error: rejection of a true null hypothesis 
Type II error: failure to reject a false null hypothesis
power: it will reject a false null hypothesis