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Sagot :
For two-sample t-tests, results cannot be compared between studies. The test statistic is given, as well as a p-value. This test is also known as a matched sample or paired t-test. It is assumption of normal distribution.
- Paired t -test:
The paired-samples t-test, also known as the dependent-samples t-test, is a statistical technique used to determine whether the mean difference between two sets of observations is zero. In a paired-samples t-test, each subject or entity is measured twice to obtain a pair of observations. Common uses of the paired-samples t-test are case-control studies or repeated measures designs.
- Normal distribution:
A normal distribution is a type of continuous probability distribution in which most data points cluster toward the middle of the range and the rest decrease symmetrically toward one extreme. The middle of the range is also called the mean of the distribution.
The normal distribution is also known as the Gaussian distribution or probability bell curve. It is symmetrical about the mean, indicating that values close to the mean occur more frequently than values far from the mean.
Assumption:
The Normalization Assumption is cautioned because so many experiments are based on the assumption of a normal distribution. is required. In most cases, the assumption of normality makes sense.
However, there is an important special case where this is not the case. Understanding the normal distribution assumptions helps researchers understand the limitations of their experiments and understand their own research and where it went wrong.
The assumption of normal distribution can be relaxed in some situations, but the analysis is more complicated. The simplest analysis is obtained when the physical process can be approximated by a normal distribution. However, even if the distribution is non-normal, it retains some basic properties. For example, you can assume symmetry, such as the t distribution, even if the distribution is not truly normal.
In fact, many different non-normal distributions are just variations of the normal distribution. For example, there are distributions with long tails that represent variations in a normal distribution. Such distributions are also common.
The reason for the normal distribution assumption is that it is usually the simplest mathematical model available. Moreover, it is remarkably ubiquitous, found in most natural and social phenomena. So the assumption of normality is usually a good first approximation.
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