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Answer :
The effect of increasing the sample size concentrates the sampling distribution around the true population proportion and generally decreases the probability of a specific outcome. Thus, option A is correct.
The probability that less than half of the sample watches news videos drops when the sample size increases from 50 to 250 adults. In general, increasing sample size concentrates the sampling distribution around the true population proportion, decreasing the probability of a specific outcome.
This question pertains to probability and sampling distribution in statistics. If the population proportion (P) is 0.54, then the probability (Q) of not watching news videos is 1-P = 0.46. For a sample of size n=50, the standard error (SE) is sqrt[PQ/n] = sqrt[(0.54*0.46)/50] = 0.070. To find the probability that fewer than half (0.5) in your sample will watch news videos, we normalize and find z = (0.5-0.54)/SE = -0.57. Using a z-table, this probability is approximately 0.2843.
When we increase the sample size to n=250, the SE = sqrt[(0.54*0.46)/250] = 0.031. The z-score is (0.5-0.54)/SE = -1.29. The probability this time is around 0.0985. Hence, the probability decreases with a larger sample size.
By concentrating the sampling distribution around the true population percentage, increasing the sample size has the effect of lowering the likelihood of a particular event. Therefore, choice A is right.
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