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You can compare the means of two dependent or independent samples. You can also set up confidence intervals. For independent samples you test the claim that the two means are not equal; the null hypothesis is mean1 equals mean2. The alternative hypothesis is mean1 does not equal mean2. For dependent (paired) samples you test the claim that the mean of the differences are not equal; the null hypothesis is the difference equals zero; the alternative hypothesis is the difference does not equal zero.
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How can you compare means of two samples when the samples are chi square distributed?
According to the Central Limit Theorem if the sample size is large enough then the means will tend towards a normal distribution regardless of the distribution of the actual sample.
What is the characteristic of a normal distribution?
The Normal (or Gaussian) distribution is a symmetrical probability function whose shape is determined by two values: the mean and variance (or standard deviation).According to the law of large numbers, if you take repeated independent samples from any distribution, the means of those samples are distributed approximately normally. The greater the size of each sample, or the greater the number of samples, the more closely the results will match the normal distribution. This characteristic makes the Normal distribution central to statistical theory.
What distribution does the F distribution approach as the sample size increases?
The F distribution is used to test whether two population variances are the same. The sampled populations must follow the normal distribution. Therefore, as the sample size increases, the F distribution approaches the normal distribution.
What are characteristics of a normal distribution?
The Normal (or Gaussian) distribution is a member of the exponential family of probability distributions. It is symmetrical function whose shape is determined by two parameters: the mean and variance (or standard deviation). The distribution s additive so that if two variables, X and Y are normally distributed then, even if their means and variances are different, their sum (and difference) are normally distributed with parameters that are simply related to the separate ones.It is not an easy distribution to calculate and so it has been necessary to tabulate key values.According to the law of large numbers, if you take repeated independent samples from any distribution, the means of those samples are distributed approximately normally. The greater the size of each sample, or the greater the number of samples, the more closely the results will match the normal distribution. This characteristic makes the Normal distribution central to statistical theory.
Why two trails of normal distribution do not touch the horizontal axis?
because it is imposible
