Now consider the Central Limit Theorem (CLT). How confidently can you use a sample from this population to make inferences about the population mean?

The central limit theorem states that if a random sample of size n is drawn from a population, then the sampling distribution of the sample mean:Group of answer choicesis approximately normal if n ≥ 30.is approximately normal if the underlying population is normal.has the same variance as the population.is approximately normal if n < 30.

The central limit theorem states that:a.The mean of a population is equal to the mean of the sampleb.The sample mean will always be equal to the population meanc.The sampling distribution of the sample mean approaches a normal distribution as the sample size increasesd.The sample mean is always greater than the population mean

Central Limit Theorem for sampling distribution is valid only when:Select one:a. Large samples drawn from any independent and identically distributed populationb. The population follows normal distributionc. Samples are drawn from any independent and identically distributed populationd. The population follows a normal distribution and the sample size should be at least 30

According to the Central Limit Theorem, the sampling distribution of the sample mean becomes approximately normally distributed asthe standard error increasesthe population variance decreasesthe size of the population increasesthe number of  samples drawn increasesthe size of the sample increases

What is the importance of having a simple random sample to ensure the CLT can be used confidently?