Statistical Consultation Line: (865) 742-7731
Accredited Professional Statistician For Hire
  • Contact Form

Hypothesis testing

Hypothesis testing is a cornerstone of empirical reasoning as it relates to using inferential statistics

Hypothesis testing is a means for communicating the results of research studies to colleagues and the targeted audience in a relative context where they can be replicated or applied in other environments. It is never feasible for researchers to collect data from ABSOLUTELY EVERY MEMBER OF A POPULATION, therefore, they take representative samples from a population and make INFERENCES based on that sample's findings BACK to the population of interest. Hypothesis testing therefore allows researchers to make inferences about populations.

The first step in hypothesis testing is to state the null hypothesis. The null hypotheses states that there is no difference or association between the phenomena or variables in the population of interest. It could also be stated as meaning that a treatment will have no effect on a population.

The second step is to state the alternative or research hypothesis. This is the whole reason for conducting a research study. Researchers believe, through a review of the literature and your clinical expertise, that there is going to be a difference or association between the variables. Or, they hypothesize that the treatment will have some sort of effect on members of the population.

The third step is to set the criteria for making a decision to either "do not reject" or "reject" the null hypothesis based on the observed values in the sample. This is called the alpha level and it is defined as the risk that researchers are willing to take to have a Type I error (false positive). It is also known as the level of significance that needs to be achieved in order to assume significant effects of associations between variables in inferential statistics. Most alpha values are set at .05. The area below the value of .05 is called the critical area and denotes statistical significance.

The fourth step is to collect sample data and run inferential statistics in order to either "do not reject" or "reject" the null hypothesis. If the statistical test yields a p-value BELOW .05, then it is statistically significant and researchers "reject" the null hypothesis. If the p-value is ABOVE .05, then it is not statistically significant, and researchers "do not reject" the null hypothesis.

Components of hypothesis testing

Click on a button below to continue.
Null Hypothesis
Research Hypothesis
Type I Error
Type II Error
Type III Error
Alpha Level
Beta Level
One-Sided Hypothesis
Two-Sided Hypothesis

Back to Statistics
Research Engineer Home Page

Hire A Statistician

$100.00
Buy Now

DO YOU NEED TO HIRE A STATISTICIAN?

Eric Heidel, Ph.D., PStat will provide you with statistical consultation services for your research project at $100/hour. Secure checkout is available with Stripe, Venmo, Zelle, or PayPal.

  • Statistical Analysis on any kind of project
  • Dissertation and Thesis Projects
  • DNP Capstone Projects
  • Clinical Trials
  • Analysis of Survey Data

Hypothesis testing allows for researchers to communicate the results of their studies to other researchers. There are several components to hypothesis testing: Null hypothesis, research hypothesis, Type I error, Type II error, Type III error, alpha level, beta level, one-sided hypothesis, and two-sided hypothesis.

Contact Dr. Eric Heidel
[email protected]
(865) 742-7731

Copyright © 2024 Scalë. All Rights Reserved. Patent Pending.