Week 3: T-Score vs. Z-Score Testing Research Assignment

Week 3
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Week 3
T-Score vs. Z-Score Testing
A z-score and a t-score are both measures employed in hypothesis testing. Ideally, t scores are used in circumstances where the sample size is below 30 and where the standard deviation is not definite. This means that for the z-score to be used, the researcher has to have a sample size that is above 30 and have clear information of the standard deviation of the population being investigated. Ideally, z-scores are a conversion of personal scores into an ordinary form. The conversion is therefore given on the basis of the knowledge that the researchers on the population’s size deviation mark and mean. The z-score gives information on the standard deviation from the average of the final result (Larsen, & Marx, 2000).
Just like the z-score, the t-score is also used as a conversion of personal scores into a standard form. However, t-scores are utilized when the conversion is made in cases where the researcher does not have prior knowledge of the sample populations’ standard deviation and mean score. Due to the lack of knowledge on the parameters of the sample population, the researcher ends up making estimates through the use of statistics from the sample population. In cases where the population is higher than 30, the researcher should use a z-test regardless of whether the population standard deviation is known or not. The research student should therefore know how to use these test scores in an efficient manner in order to make his research authentic (Larsen, & Marx, 2000).
Understanding the Alpha Level
The significance level, usually called the alpha (α) is termed as the probability of declining the null hypothesis in cases where it is true. An example of this is where a significance level of 0.05 denotes that there is a 5% risk of making conclusions to the effect that a variation exists when there is no actual variation. In most cases, researchers use an alpha level of 0.05 for testing their hypothesis but there are instances where a much lower or higher score is encouraged (Shuttleworth, 2008).
The significance test is used by researchers to establish whether the null hypothesis is rejected in favor of the substitute research theory or not. These tests in the real sense are used in establishing whether there is a connection between the various variables being tested or to establish whether a pure chance could bring about the produced results. The alpha level is much important in the sciences as it eliminates the likelihood of a test being gotten by chance. Ideally, a 0.05 level of testing the hypothesis means that the element of the results being attained due to probability is eliminated. In some cases such as when trying to establish whether a certain vaccine is ideal for human use, it is ideal to use an even lesser significance score. This is because such a research is sensitive and requires a high level of accuracy. This is mostly applicable in tests that involve the sci...