Can you explain the central limit theorem using the example of a group of coin flip trials (our population) where each trial has the same number of tosses (our sample size), and we are looking at the number of heads we get per trial, but increasing our sample size produces a normal distribution (because increasing the number of coin flips per trial decreases the likelihood that we will get extreme results on a trial--such as no heads or all heads)?

A surprising fact is that only 56% of all teens use a password on their mobile device. If a poll chooses a simple random sample of teens and asks if they use a password on their mobile device, the percent who say “Yes” will vary from sample to sample, if the sampling is repeated. In fact, if we look at the sampling distribution in this case, we will see that the percent “Yes” in many samples of the same size will follow a Normal distribution, with a mean of 56% and a standard deviation of 1.6% Although the results will vary if the poll is repeated, the distribution of results is centered at the truth about the population(56 percent). We call this desirable property of a simple random sample A)The confidence level B) low variability C)lack of bias D)symmetry

If two samples are selected from the same population, under what circumstances will the two samples have exactly the same t statistic? Question 4 options: If the samples are the same size and have the same variance If the samples are the same size and have the same mean If the samples have the same mean and the same variance If the samples are the same size and have the same mean and the same variance

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