# Explain why t distributions tend to be flatter and more spread out than the normal distribution

At the same time, it also has some chance of being much, much larger than its mean.

## Similarities between standard normal distribution and t distribution

Considered in terms of the reciprocal of the denominator whuber suggested in comments that it might be more illuminating to look at the reciprocal of the denominator. But exactly how much smaller? The right-skew in the denominator make the t-statistic heavy-tailed. By Consumer Dummies The t-distribution is a relative of the normal distribution. At the same time, it also has some chance of being much, much larger than its mean. Gosset worked out the t distribution and associated statistical tests while working for a brewery in Ireland. Now consider the case in which you have a normal distribution but you do not know the standard deviation. However, as the degrees of freedom become large, the distribution becomes much more normal-looking and much more "tight" around its mean. Intuitively, it makes sense that the probability of being within 1. The t distribution approaches the normal distribution as the degrees of freedom increase. Given different t-distributions with the following degrees of freedom, which one would you expect to most closely resemble the Z-distribution: 5, 10, 20, 30, or ? These are the values of t that you use in a confidence interval. Eventually - as Slutsky's theorem might suggest to us could happen - the effect of the denominator becomes more like dividing by a constant and the distribution of the t-statistic is very close to normal. Solve the following problems about the t-distribution, its traits, and how it compares to the Z-distribution. Answer: As the degrees of freedom increase, the t-distribution tends to look more like the Z-distribution.

Eventually - as Slutsky's theorem might suggest to us could happen - the effect of the denominator becomes more like dividing by a constant and the distribution of the t-statistic is very close to normal.

When the d.

## The denominator of the t statistic contains the

So the t-distribution with the highest degrees of freedom most resembles the Z-distribution. So if we draw randomly from the distribution of this t-statistic we have a normal random number the first term in the product times a second randomly-chosen value without regard to the normal term from a right-skew distribution that's 'typically' around 1. Under the same set of assumptions, the denominator is an estimate of the standard deviation of the distribution of the numerator the standard error of the statistic on the numerator. If you need more practice on this and other topics from your statistics course, visit 1, Statistics Practice Problems For Dummies to purchase online access to 1, statistics practice problems! B The t-distribution has a proportionately larger standard deviation than the Z-distribution. Because of a contractual agreement with the brewery, he published the article under the pseudonym "Student. The values in Table 1 can be obtained from the " Find t for a confidence interval" calculator. It has a high chance of being less than its mean, and a relatively good chance of being quite small. A The t-distribution has thicker tails than the Z-distribution. It is independent of the numerator. Notice that with few degrees of freedom, the values of t are much higher than the corresponding values for a normal distribution and that the difference decreases as the degrees of freedom increase.

Compared to the Z-distribution, the t-distribution has thicker tails and a proportionately larger standard deviation. Answer: As the degrees of freedom increase, the t-distribution tends to look more like the Z-distribution.

The second term the square root of a scaled inverse-chi-squared random variable then scales that standard normal by values that are either larger or smaller than 1, "spreading it out". Now consider the case in which you have a normal distribution but you do not know the standard deviation.

In general, the t-distribution is bell-shaped but is flatter and has a lower peak than the standard normal Z- distribution, particularly with smaller degrees of freedom for the t-distribution. This is a difficult problem because there are two ways in which M could be more than 1. Gosset worked out the t distribution and associated statistical tests while working for a brewery in Ireland.

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