# The Null Hypothesis

4-Each alpha level is dependent on the circumstance that surrounds a particular study. The significance level(alpha) is the probability of committing a type 1 error. A type 1 error is committed when the researcher falsely rejects the null hypothesis. A significance level of 0.05 is the standard situation, most especially in the field science.

There are some experiments where you would most likely want to lower the type 1 error rate such as experiment that affects human health, like drug research or studies of psychological treatment. For some experiments, if the consequence of applying null hypothesis is extremely serious, for instance, if null hypothesis applies, there may be death, or serious injury, then you want to try your best to avoid the type I error. That means you must avoid the situation that null hypothesis is true but you reject it. As the significance level is the probability, you will make the type 1 error. So, for such experiments with serious results, we want to make the level smaller than standard situation. So, for such experiments, if you can’t tolerate a 5% chance of being wrong, use a lower significance level, 0.01 for example. 0.01 is common if there’s a possibility of death or serious disease or injury.

If the consequences of being wrong are especially minor such as political research or animal migration studies. you might use a higher significance level, such as 0.1, but this is rare in practice. That is, it may be common that we make the significance level much smaller than 0.05, but we rarely make the level larger than 0.05.

Reference

Hypothesis Testing (cont…) |n.d.| Access Retrieved on 08/08/2018 from https://statistics.laerd.com/statistical-guides/hypothesis-testing-3.php

The idea of significance test. Retrieved on 08/08/2018 from https://www.khanacademy.org.

5-The alpha is the level of statistical significance. It can be any number between 0-1. 0.10, 0.05 and 0.01 are most commonly used. A situation where we would want to accept a higher alpha level is with medical testing. We would much rather have false positive test results that would lead to additional testing, even though it is going to give our patients an insane amount of anxiety. It is better than a false negative where no further testing or treatment would be indicated, and the patient would go untreated.

References

Taylor, C. (2013, March 20). What Level of Alpha Determines Statistical Significance? Retrieved from https://www.thoughtco.com/what-level-of-alpha-determines-significance-3126422

6-Not all results of hypothesis tests are equal. A hypothesis test or test of statistical significance typically has a level of significance attached to it. This level of significance is a number that is typically denoted with eh Greek letter alpha Many journals throughout different disciplines define that statistically significant results are those for which is equal to 0.05 or 5%.

The number represented by  is a probability, so it can take a value of any nonnegative real number less than one. Although in theory any number between 0 and 1 can be used for , when it comes to statistical practices this is not the case. Of all levels of significance, the values of 0.10, 0.05, and 0.01 are the most commonly used .

In medical screening for a disease, consider the possibilities of a test that falsely tests positive for a disease with one that falsely tests negative for a disease; a false positive will result in anxiety for our patient but will lead to other tests that will determine that verdict of our test was indeed incorrect; a false negative will give our patient the incorrect assumption that he does not have a disease when he in fact does. The result is that the disease will not be treated; given the choice, scientists would rather have conditions that result in a false positive than a false negative.

Reference

What Level Of Alpha Determines Statistical Significance? |June 25, 2018| Access Date| August 6, 2018 from

Courtney Taylor – https://www.thoughtco.com/what-level-of-alpha-determines-significance-3126422

Hypothesis Testing (cont…) |n.d.| Access Date August 6, 2018| from