Question

In reverse-engineering a double coin flip differential model, what would be the amount of truthful "yes/no" responses in our dataset?

A: 50%
B: 25%
C: 100%
D: 75%

Answer 1

For a double coin flip differential model, if the coins are fair, each possible outcome (HH, HT, TH, TT) should occur 25% of the time, leading to 100% truthful responses in a dataset that accurately reflects these expectations.

Step-by-step explanation:

The question 'In reverse-engineering a double coin flip differential model, what would be the amount of truthful "yes/no" responses in our dataset?' implies that we are trying to validate the fairness of a coin based on the expected outcomes of a series of coin flips. When flipping a fair coin two times, the sample space consists of the following outcomes: {HH, HT, TH, TT}. In a fair scenario, we expect the proportion for each of these outcomes to be equal, that is 25%. In the case where we have a dataset of coin flips and we are checking against the expected fair distribution, we would anticipate 25% of the responses corresponding to each of the outcomes. Therefore, in a dataset that perfectly matches the expected outcomes of a fair coin flip, the amount of truthful or correct "yes" responses that reflect this expectation would be 100%.

Answer 2

B. In reverse-engineering a double coin flip differential model, the amount of truthful "yes/no" responses in our dataset would be 25% for each outcome.

Step-by-step explanation:

The question pertains to a scenario of reverse-engineering a double coin flip differential model and asks for the amount of truthful "yes" or "no" responses in a dataset. Since the question is about the expected results from flipping two fair coins, we can refer to the sample space which contains four outcomes: {HH, HT, TH, TT}. If the coins were fair, we would expect each outcome to occur with equal probability, which is 25% for each of the four outcomes. Therefore, the amount of truthful "yes" responses in the case of a double coin flip, meaning the expected distribution, is 25% for each outcome.

In the given dataset of a double coin flip differential model, there are 4 possible outcomes: HH, HT, TH, and TT. Since each coin flip has two possible outcomes (heads or tails), the total number of outcomes for a double coin flip is 2 x 2 = 4.

Therefore, the amount of truthful "yes/no" responses in the dataset would be 4.