 # Data Science different topic explanation -- Part-3 -- Event Types

This is Data Science different topic explanation conversation series.
If you are not following this conversation series below are the link:
Part-1:

Part-2:

# Data Science different topic explanation – Part-3 – Event Types

## Independent and Dependent events

Two random events A and B are called independent if the occurrence of one of them does not change the probability of the occurrence of the other. Otherwise, events A and B are called dependent.

Counterintuitively, knowing that the coin landed on the head on the first toss, does not provide any useful information for determining what the coin will land on the next toss. The probability of a head or a tail on the next toss is still 1/2, regardless of the outcome of the first toss.

Probabilities of independent events should be multiplied to get the total probability of the occurrence of all of them.

Make sure if you are reading the post give us a like for support and motivation.

Continue above post.

Example: What are the chances of getting heads 3 times in a row?

Let’s define possible outcomes of 3 coin tosses (H-Heads, T-tails):

HHH, HHT, HTH, THH, TTH HTH, HTT, TTT

Number of ways it can happen: 1

Total number of outcomes: 8

So, the answer is 1/8. However, we know that the results of a coin toss are independent and we can multiply them to get the total probability: P(3x heads) = P(heads)P(heads)P(heads) = 1/2 * 1/2 * 1/2 = 1/8

On the other hand, knowing that the first card drawn from a deck is an ace does provide useful information for calculating the probabilities of drawing yet another ace going to be 3 over 51, instead of 4 over 52 because we know that we already remove on of the aces from the deck. You can think about that in your next poker game.