Probability and Likelihood are often considered as the same thing, which is incorrect when we come to pure statistics. There’s a clear difference between the two. Read on!

Consider a dataset of heights of kids. Assume it’s a normal distribution (for ease of understanding) with standard deviation of 2.5, the mean height is 145 cm, least value is 120 cm and the maximum value is 165 cm.

Now, what will be the probability of height being more than 150 cm if you pick a kid at random? It will be the area under the distribution curve from the point 150 cm to the maximum point.

We calculated-

P(Height>150 cm | mean=150,SD=2.5) which is read as probability of height being more than 150 cm GIVEN that mean = 150 cm & SD = 2.5. We can keep on calculating probabilities of different heights, but the mean and SD of our distribution will remain FIXED.

Now, considering the same dataset, I ask you what mean and SD of a distribution curve will best represent/fit the data? Now you’ll have to calculate the “Likelihood” of data being of a certain distribution, GIVEN a data point. The equation will be:

L(mean=150 cm,SD=2.5 | height = 148 cm)

So the data point is fixed but we change the mean and SD to find the Maximum Likelihood, L.

#statistics #datascience