Question

Flip a coin 10 times. Put a 1 each time the coin comes up heads and a 0 each time the coin comes up tails. Count the number of heads you obtained and divide by 10. What number did you get?

a. Is the number you obtained in part (a) a parameter or a statistic?

b. Now flip the coin 25 times. Put a 1 each time you obtain a heads and a 0 for tails. Count the number of heads you obtained and divide by 25.

c. What is the chance you obtain a heads in a fair coin flip? How do you believe this number is obtained using your results from parts (a) and (c)?

d. How does this process relate to the idea of a sampling distribution?

Answer 1

Following are my results when i flip a coin 10 times

0 0 1 1 0 1 1 0 1 1

heads: 6 times,   Tales: 4 times

by dividing with 10, we get 6/10 = 0.6

a) the above number 0.6 represents a characteristic of the sample of 10 coins, hence it is a statistic.

b) Following are the results, when i flip a coin 25 times

0 1 0 0 1 0 1 0 1 1 1 0 0 1 0 0 0 1 0 1 0 1 0 1 1

heads: 12 times, tables: 13 times

by dividing with 25, we get 12/25 = 0.48

c) chance of obtaining head in a fair coin flip is 0.5.

in part a) and c) we got 0.6 and 0.48 respectively. which are closer to 0.5. 0.48 is closer to 0.5 than 0.6, which means as we increase the sample size, we will get closer to 0.5 (chance of obtaining a head in fair coin flip)

d) sampling distribution refers to possible results that we can get from different/every possible sample. here, we obtain results from two samples, one is 10 and other is 25. that's why this process relates to sampling distribution.