This is the second in a series of white papers published by KINDUZ Consulting on Statistical Analysis. This paper is an introduction to population and samples, why we use samples, and the various sampling strategies that can be applied.
Context for the Reader
You are the Global Chief Executive of KINDUZ Corp., a global organization with revenue of USD 10 billion. The vision of KINDUZ is “Creating Universal Prosperity”. In line with its vision, KINDUZ has established itself in multiple industries including Pharmaceuticals, Oil, and Gas, IT/ITES, Automotive manufacturing and Cement industries.
You and your entire team believe that Creating Universal Prosperity is achieved through alignment of organizational goals to individual goals of stakeholders (Employees, Stockholders, Customers, and Societies).
Approach of the Whitepaper
This whitepaper is one in a series of whitepapers published by KINDUZ to enable deeper understanding and application of tools and techniques by CEOs enabling them to deliver sustainable outcomes.
The learning in the whitepaper is structured around cases, where the concepts and its applications are explained through the case description, analysis, and solutions.
CEOs take important decisions about their organizations based on the questions they ask, the data provided in response to these questions and the analysis of this data. Typically, the data that is used for analysis is a small portion of the entire data. This entire data set is called as population and the small portion selected is called as sample.
It is important to understand why we sometimes use samples instead of the population, and having decided to use samples, what sampling technique will give us the most representative picture.
To understand God’s thoughts,
one must study statistics… the measure of his purpose.”
– Florence Nightingale
This white paper first enables a CEO to understand the difference and similarity of population and sample. Then we discuss the following sampling techniques which are categorized as Probability sampling:
- Simple random sampling
- Stratified sampling
- Systematic sampling
- Cluster sampling (single stage and two stage)
In this paper, we have focused exclusively on probability sampling techniques. The primary reason for this is that non-probability sampling does not provide an equal probability for all units to be selected and therefore it is not apt for holistic decision making process for a CEO, because the data could be biased.
For example, KINDUZ Pharma had to recall an entire batch of medicines because of assay stability failure. The team at KINDUZ Pharma wants to study why the batch failed on assay stability. If the team collects random samples from batches (including batches that passed the assay stability and the batches that did not pass the assay stability) to study the impact of process variables on assay stability, then it is Probability sampling. If the team collects data samples from the batch that failed on assay stability, then it is non-probability
sampling. The samples collected from the failed batch is not representative of the entire population (all batches produced in a specified time period) and will not enable the KINDUZ Pharma team to holistically find the causes of assay stability failure.
This paper provides five cases that explain population, samples and probability sampling techniques. The situations in which these sampling techniques should be used are summarized in the table below:
|Sampling Technique||Situation to use in|