 # From concept to finished cross-channel digital product

As a part of Google’s Data Science Interview, they like to ask questions that they call “Problem-Solving” questions which are pretty similar to brain teasers.

As a part of Google’s Data Science Interview, they like to ask questions that they call “Problem-Solving” questions which are pretty similar to brain teasers. In this article, we’ll look at six questions that Google’s asked and provide the answers below!

## 1. A box has 12 red cards and 12 black cards. Another box has 24 red cards and 24 black cards. You want to draw two cards at random from one of the two boxes, one card at a time. Which box has a higher probability of getting cards of the same color and why?

The box with 24 red cards and 24 black cards has a higher probability of getting two cards of the same color. Let’s walk through each step.

Let’s say the first card you draw from each deck is a red Ace.

This means that in the deck with 12 reds and 12 blacks, there’s now 11 reds and 12 blacks. Therefore your odds of drawing another red are equal to 11/(11+12) or 11/23.

In the deck with 24 reds and 24 blacks, there would then be 23 reds and 24 blacks. Therefore your odds of drawing another red are equal to 23/(23+24) or 23/47.

Since 23/47 > 11/23, the second deck with more cards has a higher probability of getting the same two cards.

## 2. You are at a Casino and have two dices to play with. You win \$10 every time you roll a 5. If you play till you win and then stop, what is the expected payout?

• Let’s assume that it costs \$5 every time you want to play.
• There are 36 possible combinations with two dice.
• Of the 36 combinations, there are 4 combinations that result in rolling a five (see blue). This means that there is a 4/36 or 1/9 chance of rolling a 5.
• A 1/9 chance of winning means you’ll lose eight times and win once (theoretically).
• Therefore, your expected payout is equal to \$10.00 * 1 – \$5.00 * 9= -\$35.00.

Edit: Thank you guys for commenting and pointing out that it should be -\$35!

## 3. How can you tell if a given coin is biased?

This isn’t a trick question. The answer is simply to perform a hypothesis test:

1. The null hypothesis is that the coin is not biased and the probability of flipping heads should equal 50% (p=0.5). The alternative hypothesis is that the coin is biased and p != 0.5.
2. Flip the coin 500 times.
3. Calculate Z-score (if the sample is less than 30, you would calculate the t-statistics).
4. Compare against alpha (two-tailed test so 0.05/2 = 0.025).
5. If p-value > alpha, the null is not rejected and the coin is not biased.
If p-value < alpha, the null is rejected and the coin is biased.