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Hypothesis testing is a common term in today's statistics. It is a critical procedure that is performed by statisticians when the need to make inferences about a population with the use of a random sample arises. These inferences made include estimating the properties of a population such as the means, the differences between these means, relationships between variables and the proportions.
Reasons for Performing Statistical Hypothesis Testing
Hypothesis testing helps us to draw a conclusion about a given entire population based on a random sample. When doing statistical hypothesis testing, you will gain tremendous experience on how to work with a random sample. Conventionally, it is quite impossible to map out the characteristics of an entire population. But with a collection of one sample, it becomes easier to use statistics to analyze such characteristics. Which is a wiser opinion; it is the only suitable opinion.
Although the use of a sample to study a character of an entire population is less expensive and more practical, there are tradeoffs. When the properties of a population are estimated using a sample, this sample statistics are unlikely to give you the exact population value. For example, the sample mean is hardly equal to the population mean. This difference between the sample mean and the population mean is termed as the sampling error.
The differences observed by statisticians during research might be because of sample errors rather than the actual effect of the represented population level. If it's the sampling error that is the cause of the difference observed, the results of the next experiments may be different. Through hypothesis testing, one can be able to incorporate the estimates of these sampling errors which are helpful in making the correct decision.
Hypothesis Testing
This is a statistical analysis that is used by statisticians to assess two mutually exclusive theories of population properties by use of a sample data. These two mutually exclusive theories are known as the null hypothesis and the alternative hypothesis. Through hypothesis testing, the statistician can assess the sample and also factor in the estimation of the sample error to help him or her to determine which type of hypothesis the sample data support. When a null hypothesis is rejected, that means the results are significant, and the sample data support the theory that a particular effect indeed exists in the population
What is an Effect on Hypothesis Testing?
The effect of a population effect is the difference between the null hypothesis value and the population value. However, it is challenging to know the actual size of an effect, but through hypothesis testing, one can determine the existence of the effect and its size.
Null Hypothesis
This is one of the two mutually exclusive theories in hypothesis testing. Null hypothesis means that there is no any effect. H0 denotes hypothesis testing.
When doing a hypothesis testing, researchers are trying to establish an effect. This can be the effect of a particular product, its durability, etc. However, there may be no any effect in the experimental groups, and that lack is what is termed as the null hypothesis testing. Hence, when the null hypothesis is rejected, the alternative hypothesis is favored.
Alternative Hypothesis
Alternative hypothesis means that the population parameter is not equal to null hypothesis value. Hence it has a non-zero effect. Through sufficient evidence, a statistician can favor the alternative hypothesis and reject the null hypothesis. The alternative hypothesis is denoted as H1 or HA.
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