How to Determine Alpha Values of 5% and 1% in Hypothesis Testing

If you are conducting research, you certainly have a hypothesis for your study. Hypothesis testing is crucial in research, especially if you’re performing inferential statistical analysis. In statistical hypothesis testing, you are often faced with the choice of using an alpha value of 5% or 1% for your study.

However, many still question the reasoning behind selecting these alpha values. Based on this, I’m interested in writing an article discussing how to determine a 5% or 1% alpha value in hypothesis testing. Before diving deeper, let’s first understand the definition of hypothesis testing in research.

Understanding the Definition of Research Hypotheses

Hypothesis testing is an essential step in research aimed at testing specific assumptions about the observed population. Essentially, in hypothesis testing, we already have assumptions or predictions about the population being observed.

In research, we generally take samples from a population considering time, cost, and effort constraints. What you need to understand about hypothesis testing is that its purpose is to determine whether there is sufficient strong evidence to reject the null hypothesis.

Thus, hypothesis testing involves testing the null hypothesis to determine whether it is accepted or rejected. If we reject the null hypothesis, it means we accept the alternative hypothesis.

A simple example is when you test whether a new teaching method is more effective than the old method. You can formulate a hypothesis, such as “the new teaching method is more effective than the old method,” as your research hypothesis.

Understanding the Basic Theory of Statistical Hypotheses

Now, let’s understand the basic theory of statistical hypotheses in research. To test these hypotheses, we first need to formulate statistical hypotheses.

Statistical hypotheses can be divided into two types: the null hypothesis (H₀) and the alternative hypothesis (H₁). Essentially, statistical hypothesis testing is designed to evaluate the possibility of rejecting H₀. So, how do we formulate these statistical hypotheses?

The null hypothesis is essentially the initial statement that assumes no difference, no effect, or no correlation. For example, if we are observing the effect of organic fertilizer use on rice production, the null hypothesis can be formulated as: “There is no effect of organic fertilizer use on rice production in region ABC.”

Meanwhile, the alternative hypothesis is a statement that contradicts the null hypothesis, where its truth needs to be tested. The alternative hypothesis is the opposite of the null hypothesis. Based on the previous example, the alternative hypothesis can be formulated as: “The use of organic fertilizer increases rice production in region ABC” or “There is a significant effect of organic fertilizer use on rice production in region ABC.”

With this explanation, I hope you now understand what research hypotheses and statistical hypotheses are. Now, let’s move on to understanding the concept of alpha in research.

The Concept of Alpha in Research

As I mentioned earlier, to address your research objectives, you need to test statistical hypotheses. Statistical hypothesis testing is designed to evaluate the possibility of rejecting the null hypothesis.

To accept or reject statistical hypotheses, we need to understand the concept of alpha in research. Alpha is the significance level in hypothesis testing, representing the probability of making a Type I error. A Type I error occurs when a true null hypothesis is rejected.

Alpha values are generally expressed as percentages, such as 5% or 1%, and in social sciences, some even use an alpha value of 10%. So, what exactly does alpha indicate in research?

Alpha is the threshold for the error rate in research. For example, if we set an alpha value of 5%, it means the maximum allowable error rate in the research is 5%. For instance, in 100 trials, the maximum allowable errors would be 5.

This means that if we only make three errors out of 100 trials, it indicates that our hypothesis is accepted. Based on the title of this article, I discuss alpha values of 5% and 1%. Thus, according to this definition, an alpha value of 1% has a lower risk of making a Type I error compared to an alpha value of 5%.

However, with a 1% alpha value, it becomes more difficult to detect significant differences compared to a 5% alpha value. Therefore, we can conclude that the smaller the alpha value, the lower the risk of making a Type I error, which is rejecting a true null hypothesis.

Differences Between 5% and 1% Alpha in Research

Based on the previous subchapter I wrote, you should now be able to identify the differences between 5% and 1% alpha values. When viewed from their respective alpha values, research using a 5% alpha (0.05) has a higher tolerance for Type I errors.

In contrast, research that uses a 1% alpha (0.01) has a lower tolerance for Type I errors. So, how do we choose between a 5% or 1% alpha in research?

Using a 5% alpha in research is the most common choice in many fields of study, including economics, business, education, social sciences, and others. However, in some social science studies, where sample conditions are difficult to control, an alpha as high as 10% may be used. This, of course, means a higher tolerance for Type I errors compared to 5% and 1% alpha values.

On the other hand, a 1% alpha is generally used in studies requiring high precision results, such as medical research or physics experiments. Thus, selecting the appropriate alpha value—5% or 1%—depends greatly on the field of study you are exploring.

Other Considerations When Choosing a 5% or 1% Alpha in Research

Next, I will review other considerations when choosing between a 5% or 1% alpha in research. The first consideration is the research context. This is essentially similar to selecting the alpha value based on the field of research. For example, if the research context involves critical decisions, such as medical treatments in the healthcare field, a smaller alpha value, such as 1%, is recommended.

However, if the risk is lower, such as in a consumer survey, a larger alpha value, like 5%, can be chosen. Another consideration is sample size and data. Conceptually, the larger the sample size, the smaller the likelihood of Type I and Type II errors. Therefore, a smaller alpha value may be more appropriate in such cases.

I hope the explanations I’ve provided in this article can add insight for fellow researchers who are uncertain about whether to use a 5% or 1% alpha in their studies.

Determining the right alpha value is a crucial step in hypothesis testing. Therefore, when selecting an alpha value, we must consider factors such as the research context, the risk of errors, or the specific field of study.

By choosing the correct alpha value, researchers are expected to make more accurate and unbiased statistical decisions. That’s the article Kanda Data has prepared for this occasion. I hope it is helpful and useful for those in need. Thank you for reading this article, and stay tuned for future updates from Kanda Data.