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Does the outlier affect the standard deviation?
Yes, outliers can significantly affect the standard deviation. Since standard deviation measures the dispersion of data points from the mean, the presence of an outlier can increase the overall variability, leading to a higher standard deviation. This can distort the true representation of the data's spread and may not accurately reflect the typical data points in the dataset.
Is it true that The smaller the standard deviation of a normal curve the higher and narrower the graph?
Yes, that's true. In a normal distribution, a smaller standard deviation indicates that the data points are closer to the mean, resulting in a taller and narrower curve. Conversely, a larger standard deviation leads to a wider and shorter curve, reflecting more variability in the data. Thus, the standard deviation directly affects the shape of the normal distribution graph.
Is it true or false that the standard deviation of a set is always smaller than the variance of that set?
False. The standard deviation of a set is the square root of the variance, so it is not always smaller; in fact, it is always less than or equal to the variance when the variance is positive. The standard deviation can be equal to the variance only when the variance is 1 (since the square root of 1 is 1).
What is a population standard deviation?
The true / real standard deviation ("the mean deviation from the mean so to say") which is present in the population (everyone / everything you want to describe when you draw conclusions)
When calculating the confidence interval why is the sample standard deviation used to derive the standard error of the mean?
The sample standard deviation is used to derive the standard error of the mean because it provides an estimate of the variability of the sample data. This variability is crucial for understanding how much the sample mean might differ from the true population mean. By dividing the sample standard deviation by the square root of the sample size, we obtain the standard error, which reflects the precision of the sample mean as an estimate of the population mean. This approach is particularly important when the population standard deviation is unknown.
Does the outlier affect the standard deviation?
Yes, outliers can significantly affect the standard deviation. Since standard deviation measures the dispersion of data points from the mean, the presence of an outlier can increase the overall variability, leading to a higher standard deviation. This can distort the true representation of the data's spread and may not accurately reflect the typical data points in the dataset.
True or false a distribution of sample means is normally distributed with a mean equal to the population mean and standard deviation equal to the standard error of the mean?
True.
Is standard deviation same as standard error?
From what ive gathered standard error is how relative to the population some data is, such as how relative an answer is to men or to women. The lower the standard error the more meaningful to the population the data is. Standard deviation is how different sets of data vary between each other, sort of like the mean. * * * * * Not true! Standard deviation is a property of the whole population or distribution. Standard error applies to a sample taken from the population and is an estimate for the standard deviation.
Is it true that The smaller the standard deviation of a normal curve the higher and narrower the graph?
Yes, that's true. In a normal distribution, a smaller standard deviation indicates that the data points are closer to the mean, resulting in a taller and narrower curve. Conversely, a larger standard deviation leads to a wider and shorter curve, reflecting more variability in the data. Thus, the standard deviation directly affects the shape of the normal distribution graph.
Is it true that the standard deviation is equal to the square root of the variance?
Yes. Please see the related link, below.
Is it true or false that the standard deviation of a set is always smaller than the variance of that set?
False. The standard deviation of a set is the square root of the variance, so it is not always smaller; in fact, it is always less than or equal to the variance when the variance is positive. The standard deviation can be equal to the variance only when the variance is 1 (since the square root of 1 is 1).
What is true of a standard normal probability distribution aA mean of 0 and a standard deviation of 1 b A mean of 1 and any standard deviation of 1 c Any mean and a standard deviatio?
a is true.
What is a population standard deviation?
The true / real standard deviation ("the mean deviation from the mean so to say") which is present in the population (everyone / everything you want to describe when you draw conclusions)
Is it true that the larger the standard deviation the wider the confidence interval?
no
How does a sample size impact the standard deviation?
If I take 10 items (a small sample) from a population and calculate the standard deviation, then I take 100 items (larger sample), and calculate the standard deviation, how will my statistics change? The smaller sample could have a higher, lower or about equal the standard deviation of the larger sample. It's also possible that the smaller sample could be, by chance, closer to the standard deviation of the population. However, A properly taken larger sample will, in general, be a more reliable estimate of the standard deviation of the population than a smaller one. There are mathematical equations to show this, that in the long run, larger samples provide better estimates. This is generally but not always true. If your population is changing as you are collecting data, then a very large sample may not be representative as it takes time to collect.
When calculating the confidence interval why is the sample standard deviation used to derive the standard error of the mean?
The sample standard deviation is used to derive the standard error of the mean because it provides an estimate of the variability of the sample data. This variability is crucial for understanding how much the sample mean might differ from the true population mean. By dividing the sample standard deviation by the square root of the sample size, we obtain the standard error, which reflects the precision of the sample mean as an estimate of the population mean. This approach is particularly important when the population standard deviation is unknown.
Why standard deviation is more often used than variance?
Both variance and standard deviation are measures of dispersion or variability in a set of data. They both measure how far the observations are scattered away from the mean (or average). While computing the variance, you compute the deviation of each observation from the mean, square it and sum all of the squared deviations. This somewhat exaggerates the true picure because the numbers become large when you square them. So, we take the square root of the variance (to compensate for the excess) and this is known as the standard deviation. This is why the standard deviation is more often used than variance but the standard deviation is just the square root of the variance.
