About 98% of the population.
When the standard deviation of a population is known, the sampling distribution of the sample mean will be normally distributed, regardless of the shape of the population distribution, due to the Central Limit Theorem. The mean of this sampling distribution will be equal to the population mean, while the standard deviation (known as the standard error) will be the population standard deviation divided by the square root of the sample size. This allows for the construction of confidence intervals and hypothesis testing using z-scores.
Also normally distributed.
The answer depends on what SAT tests. In the UK the mean is 100 and the SD approx 15 - the scores are truncated at 100 +/- 44.
When the population standard deviation is known, the sample distribution is a normal distribution if the sample size is sufficiently large, typically due to the Central Limit Theorem. If the sample size is small and the population from which the sample is drawn is normally distributed, the sample distribution will also be normal. In such cases, statistical inference can be performed using z-scores.
A t-test is performed instead of a z-test when the sample size is small (typically n < 30) and the population standard deviation is unknown. The t-test accounts for the increased variability and uncertainty in small samples by using the sample standard deviation rather than the population standard deviation. Additionally, it is often used when the data is approximately normally distributed.
99.6% for
68.2%
The Z test.
The mean and standard deviation. If the data really are normally distributed, all other statistics are redundant.
True.
No, but the approximation is better for normally distributed variables.
When the standard deviation of a population is known, the sampling distribution of the sample mean will be normally distributed, regardless of the shape of the population distribution, due to the Central Limit Theorem. The mean of this sampling distribution will be equal to the population mean, while the standard deviation (known as the standard error) will be the population standard deviation divided by the square root of the sample size. This allows for the construction of confidence intervals and hypothesis testing using z-scores.
Also normally distributed.
A particular fruit's weights are normally distributed, with a mean of 760 grams and a standard deviation of 15 grams. If you pick one fruit at random, what is the probability that it will weigh between 722 grams and 746 grams-----A particular fruit's weights are normally distributed, with a mean of 567 grams and a standard deviation of 25 grams.
The answer depends on what SAT tests. In the UK the mean is 100 and the SD approx 15 - the scores are truncated at 100 +/- 44.
When the population standard deviation is known, the sample distribution is a normal distribution if the sample size is sufficiently large, typically due to the Central Limit Theorem. If the sample size is small and the population from which the sample is drawn is normally distributed, the sample distribution will also be normal. In such cases, statistical inference can be performed using z-scores.
square (25/36) = 5/6 = .833