T-distributions tend to be flatter and more spread out than normal distributions due to their heavier tails. Unlike the normal distribution, which has thin tails, t-distributions account for uncertainty in sample variance estimation, making them more robust for smaller sample sizes. The additional variability inherent in t-distributions arises from the incorporation of the sample size through the degrees of freedom parameter. As the degrees of freedom decrease, the t-distribution becomes more spread out and flatter, reflecting increased uncertainty and variability in the estimates. This property makes t-distributions well-suited for inferential statistics, particularly when dealing with small sample sizes.
It is a measure of the spread of the distribution: whether all the observations are clustered around a central measure or if they are spread out.
Population distribution refers to the patterns that a population creates as they spread within an area. A sampling distribution is a representative, random sample of that population.
Outliers will make give the graph a long tail (or tails). Overall, the graph will be flatter and wider.
It is a measure of the spread of the distribution. The greater the standard deviation the more variety there is in the observations.
An uneven distribution means that an area which is uneven to the area beside the area which is uneven
The standard deviation (SD) is a measure of spread so small sd = small spread. So the above is true for any distribution, not just the Normal.
Yes, the normal distribution is uniquely defined by its mean and standard deviation. The mean determines the center of the distribution, while the standard deviation indicates the spread or dispersion of the data. Together, these two parameters specify the shape and location of the normal distribution curve.
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The normal distribution allows you to measure the distribution of a set of data points. It helps to determine the average (mean) of the data and how spread out the data is (standard deviation). By using the normal distribution, you can make predictions about the likelihood of certain values occurring within the data set.
The two distributions are symmetrical about the same point (the mean). The distribution where the sd is larger will be more flattened - with a lower peak and more spread out.
It is a measure of the spread of the distribution: whether all the observations are clustered around a central measure or if they are spread out.
With a 10 point grading scale the results (of a test etc.) are given a value between 0 and 9 or 1 and 10. If the grading is "on a curve" than the distribution of the various grades is spread on a Gaussian normal distribution.
Yes. By definition. A normal distribution has a bell-shaped density curve described by its mean and standard deviation. The density curve is symmetrical(i.e., an exact reflection of form on opposite sides of a dividing line), and centered about (divided by) its mean, with its spread (width) determined by its standard deviation. Additionally, the mean, median, and mode of the distribution are equal and located at the peak (i.e., height of the curve).
In a statistical sense, spread, otherwise known as statistical dispersion, is one of various measures of distribution.
Population distribution refers to the patterns that a population creates as they spread within an area. A sampling distribution is a representative, random sample of that population.
Spread, in the context of a probability distribution, is a measure of how much the data vary about their central value.
Outliers will make give the graph a long tail (or tails). Overall, the graph will be flatter and wider.