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The alpha, also known as significance level or α, is a measure of the strength of the evidence that must be present in your sample before you will reject the null hypothesis and conclude that the effect is statistically significant. 

Common Uses of Alpha

It is commonly used to determine the significance level before conducting the experiment. The alpha or significance level, also known as the “Type I error rate,” is an important concept in statistical testing. It is the probability of rejecting a true null hypothesis. In other words, it is the probability of making a Type I error in your analysis – that is, concluding that there is an effect or difference when in fact there isn’t. In most cases, researchers use α = 0.05 (5%) as the standard cut-off for their statistical analysis (though other values are sometimes used). 

When conducting a hypothesis test with a 5% alpha level, if the calculated p-value is less than 0.05, then the null hypothesis can be rejected and it can be said that there is a statistically significant difference between two groups or conditions. This means that if you conduct 100 tests with this alpha level, you can expect to make five errors where you reject the null hypothesis incorrectly and 95 times where you correctly accept or reject the null hypothesis.  To put it another way, an alpha of 0.05 implies that there’s only a 5% chance of making an incorrect decision when rejecting the null hypothesis – so there’s a 95% chance of making the correct decision based on your data analysis results. Therefore, choosing an appropriate alpha value should take into account both what magnitude of false positives and false negatives you can tolerate in your study design and analysis process. The chosen significance level will also have an impact on power analysis – which determines how many samples are needed to achieve statistical significance with high accuracy – and determines how reliable your study results will ultimately be. Having overly stringent requirements for alpha can lead to high power values but also more false positives; conversely having too low of an alpha value could result in lower power but fewer false positives.  


In summary, choosing an appropriate alpha value depends on many factors such as sample size, type of research question being asked and desired outcomes from the study itself; understanding how to best approach these decisions will ultimately depend on how well-informed one is about their research question and its associated variables as well as theories related to design and statistic principles used to answer them accurately and reliably.


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