Probability and Sampling Distribution
Description
Probability and sampling distributions are foundational concepts in statistics. They play a crucial role in understanding data and making inferences about populations from samples. Probability is the measure of the likelihood that an event will occur. It quantifies uncertainty and is expressed as a number between 0 and 1, where 0 indicates that an event will not occur, and 1 indicates that an event will certainly occur.
A sampling distribution is the probability distribution of a statistic (like the mean, variance, proportion) calculated from many samples drawn from a specific population. It shows how the statistic would vary if we take many samples from the same population.
$395 (Fee covers training materials)
September 18, 19, and 21, 2026 10 am - 12 pm MT
Purpose
If you have limited knowledge of probability and sampling distribution or you are just inquisitive to know what it is all about, this training is for you. Beginning with the definitions of probability and sampling distributions, we introduce you to the key concepts in probability, probability rules, application of probability and sampling distributions. We would not let you go until you understand Bayes’ theorem and its applications.
Expected Learning Outcomes
By the end of this training, you will be able to
explain the concept of random variables;
recognize the distinction between samples and populations;
distinguish between different types of samples;
discuss sampling distribution, variance, and the central limit theorem;
tell and interpret probabilities from a sampling distribution; and
explain the effects and applications of normality and skewness.
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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Why this course
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