What is Alpha in statistics?

The level of significance, also known as Alpha or α, is “the probability of rejecting the null Hypothesis Test when it is true”. For example, a level of significance of Alpha=0.05 indicates “a 5% risk of concluding that a difference exists when there is no actual difference”.

 Confidence Interval: 

Confidence Interval refers to the possibility of a parameter that lies within a specified range of values, which is denoted as c. Moreover, the Confidence Interval is connected with the level of significance. The relationship between level of significance and the Confidence Interval is c=1−α.

• The level of significance 0.10 is related to the 90% Confidence Interval.
• The level of significance 0.05 is related to the 95% Confidence Interval.
• The level of significance 0.01 is related to the 99% Confidence Interval.

The level of significance is “the probability of making the wrong decision when the null hypothesis test is true. Alpha levels are used in Hypothesis Tests. Usually, these tests are run with an Alpha=0.05, but other levels commonly used are Alpha=0.01 and .10”.

When a P-value in statistics is “less than or equal to the level of significance”, you reject the null Hypothesis Test. If we take the “P-value in statistics for our example and compare it to the common significance levels, it matches the previous graphical results. The P-value in statistics of 0.03112 is statistically significant at an Alpha=0.05, but not at the Alpha=0.01 level”.

If we stick to Alpha=0.05, we can conclude that the average energy cost for the population is greater than 260.

Reducing the Alpha level from Alpha=0.05 to Alpha=0.01 reduces the chance of a false positive. to describe the strength of evidence, provide by a p-value in statistics in different categories:

• Alpha > 0.1: No evidence
• Alpha between Alpha=0.05 and Alpha=0.1: Weak evidence
• Alpha between Alpha=0.01 and Alpha=0.05: Evidence
• Alpha between Alpha=0.001 and Alpha=0.01: Strong evidence
• Alpha < 0.001: Very strong evidence

Lower Alpha levels are sometimes used when you are carrying out multiple tests at the same time. A common approach is to divide the Alpha level by the number of tests being carried out. For example, if you needed to carry out 5 tests you might set your initial Alpha level at Alpha=0.05 then divide it by 5 to obtain the Alpha level of Alpha=0.01.

Although Alpha=0.05 and Alpha=0.01 are values commonly used for Alpha, there is no overriding mathematical theorem that says these are the only level of significance that we can use. 

Why is an Alpha level of .05 commonly used?

Seeing as the Alpha level is “the probability of making a Type I error. For example, if we set the Alpha level at 10% then there is large chance that we might incorrectly reject the null Hypothesis Test, while an Alpha=0.01 would make the area tiny. So why not use a tiny area instead of the standard Alpha=0.05?”

 Statistically significant – What Does It Really Mean? 

Statistically significant is the probability of finding a given deviation from the null Hypothesis Test -or a more extreme one- in a sample.

Statistically significant is often referred to as the p-value in statistics (short for “probability value”) or simply p in research papers.

Discussion about Statistically significant Results

Statistically significant is “the likelihood that a relationship between two or more variables is caused by something other than chance. A test result is statistically significant when the sample statistic is unusual enough relative to the null Hypothesis Test that we can reject the null Hypothesis Test for the entire population”.

The common Alpha values of Alpha=0.05 and Alpha=0.01 are simply based on tradition. For level of Alpha=0.05, expect to obtain sample means in the critical region 5% of the time when the null Hypothesis Test is true.

A data set is statistically significant when the set is large enough to accurately represent the phenomenon or population sample being studied.  A data set is typically deemed to be statistically significant if the probability of the phenomenon being random is less than 1/20. In general, Alpha=0.05 or lower is considered to be statistically significant.

Statistically significant is often used for new pharmaceutical drug trials. Statistically significant is used to accept or reject the null Hypothesis Test, which hypothesizes that there is no relationship between measured variables. Statistically Hypothesis Test is used to determine whether the result of a data set is statistically significant.

 Watch: P Values, z Scores, Alpha, Critical Values 

With greetings: Al - Manara Consulting to help researchers and graduate students