Principles of Hypothesis Testing
Description
Hypothesis testing is all about making an assumption (hypothesis), gathering information relating to the hypothesis made and using statistical techniques to either prove or disprove the assumption made. In inferential statistics, many techniques have an underlying hypothesis. It is important to understand the hypothesis being tested in order to interpret the results correctly and make right decisions.
Purpose
This training equips you with the knowledge and skills to test hypotheses so as to draw conclusions about a population parameter, minimize likelihood of making decisions attributed to chance occurrence only, and evaluate whether the observed data supports or contradicts a specific hypothesis about a population parameter.
$395 (Fee covers training materials)
May 16, 18, and 22, 2026 10:00 am - 12:00 pm MT
Expected Learning Outcomes
By the end of this training, you will be able to
explain probability distributions in the context of hypothesis testing, especially in drawing conclusions and types of errors;
determine when to apply the test;
discuss the assumptions of one sample t test, two-independent Samples t-test, and Paired samples t-test;
explain the reasons for testing hypotheses;
identify the steps in hypothesis testing;
recognize types of explanatory and response variables; and
explain the relationship between confidence interval and hypothesis testing.
Target Learners
This training program is designed for individuals or groups of people who work in clinical settings, academia, pharmaceutical industries, Government, corporations, nonprofit organizations, and organizations that work on biological data.
Learning Approach
Programs are delivered through live online interactive sessions with seasoned instructors having vast experiences in academia. Contents are well structured and manageable with self-paced learning activities and exercises. Our approach provides learners opportunities for hands-on practice, asking questions, investigating problems, and applying their knowledge to solve complex scenarios or real-world problems. In the end, learners develop deeper understanding, critical thinking, and problem-solving skills.
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