Course Content
Data Visualization
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Histogram
Frequency Polygon
Difference Between Histogram and Frequency Polygon
Relative Frequency Distribution
Cumulative Frequency Table
Contingency Table (Cross-Tabulation Table)
Box and Whiskers : IQR
Probability Distributions
Probability Distribution – Binomial, Poisson, Normal, and Exponential
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Normal Distribution
Binomial Distribution
Binomial Distribution
Poisson distribution
Exponential Distribution
Operations Management Basics
0/6
Time Study
Motion Study
EOQ: Economic Order Quantity
EOQ
Process Mapping Symbols
Key Figures in Operations Management
Statistics
0/30
Measures of Central Tendency: Mean, Median & Mode
Standard Deviation
Karl Pearson’s Coefficient of Variation
Correlation Coefficient
Method of Least Square : Regression Analysis
Assumptions Regarding the Error Term in Regression
Regression Coefficient
Relationship Between Correlation Coefficient and Regression Coefficient
Positional Averages (Positional Means)
Mathematical Averages (Mathematical Means)
Mean, Median, Mode, Variance, SD
Hypothesis Testing
Hypothesis Testing
Sampling Methods
Sampling Method
Kaizen
Kaizen
Six Sigma
Types of Statistical Tests
Types of Statistical Tests
Chi-Square Goodness of Fit Test
Which one of the following is correct when a null hypothesis is accepted?
Type I & Type II Errors
Demand Forecasting
Demand Forecasting
Types of Reliability in Research
ISO 9000 Series Standards
Gant Chart
Lean Manufacturing
5S Lean Methodology
Assignment
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Complete Enumeration Method
Transportation
0/5
Tranportation Foundations
LCM: Least Cost Method
North-West Corner Method
VAM: Vogel’s Approximation Method
Transportation Quiz
Queuing
0/3
Queing Theory
Queue Discipline
Queing Theory
Project Management
0/4
PERT: Program Evaluation and Review Technique
CPM: Critical Path Method
PERT VS CPM
Project Management
ERP
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What is ERP (Enterprise Resource Planning)?
Facility Location and Layout
Site Selection and Analysis, Layout Design and Process
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Plant Layout Design
Facility Location and Layout
Line Balancing
Data Collection & Questionnaire Design
0/2
Data Collection Method
Data Collection & Questionnaire Design
Overall Quiz
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Overall Quiz
Question ID
Quality Management
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Inspection Process and Types
Unit VIII: Business Statistics and Operations Management

In statistics, Sampling methods refer to the techniques used to select a sample (a subset of the population) that represents the entire population. These methods are crucial for making inferences about a population without studying every individual.

Types of Sampling Methods in Statistics:

1. Probability Sampling Methods

In probability sampling, every member of the population has a known, non-zero chance of being selected.

  • Simple Random Sampling: Every member of the population has an equal chance of being selected. This is typically done using a random number generator or drawing names.

  • Systematic Sampling: Selects individuals at regular intervals from an ordered list. For example, every 10th person in a list of 1000 might be chosen.

  • Stratified Sampling: The population is divided into subgroups (strata) based on a specific characteristic (e.g., age, gender). Then, a random sample is taken from each subgroup. This ensures that all important subgroups are represented in the sample. Size of all strata is equal.

  • Cluster Sampling: The population is divided into clusters (e.g., geographical areas or organizations). A random sample of clusters is selected, and all individuals or a random sample within the selected clusters are studied.

  • Area Sampling: A specific type of Cluster Sampling where the population is divided into geographical areas or regions (e.g., neighborhoods or districts). Randomly selected areas are then studied by sampling individuals within them. This is commonly used in large-scale surveys that are geographically dispersed.

  • Multi-Stage Sampling: A combination of different sampling methods applied in stages. For example, in the first stage, you might select regions (clusters) using Area Sampling, then randomly select households within those areas, and further randomly select individuals within the selected households. This method is often used for large or complex populations.

2. Non-Probability Sampling Methods

In non-probability sampling, not every member of the population has a chance of being selected, and the selection process is based on the researcher’s discretion.

  • Convenience Sampling: The sample is taken from individuals who are easiest to reach. This is often the least expensive and easiest method but may introduce bias because the sample is not representative.

  • Judgmental (Purposive) Sampling: The researcher selects individuals based on their judgment or knowledge of the population. It’s often used when the researcher wants to focus on a particular group or characteristic.

  • Snowball Sampling: This method is used when studying hard-to-reach or hidden populations. One person is selected, and then they refer the researcher to other individuals. This process continues like a “snowball.”

  • Quota Sampling: The researcher selects a sample that has a certain number of individuals from specific subgroups, ensuring the sample matches certain proportions of the population, but it does not involve random selection.

  • Panel Sampling: This method involves selecting a specific group of individuals who are surveyed or observed over a long period of time. These individuals form a “panel” and are repeatedly contacted to gather data at different points. Panel Sampling is often used in market research, where a consistent group of consumers is studied over time to track changes in behavior or preferences.

Summary:

  • Probability Sampling: Every member has a known chance of being selected (more reliable and generalizable results).
  • Non-Probability Sampling: Selection is based on judgment or convenience (less reliable but faster and cheaper).

The choice of sampling method depends on the research goals, available resources, and the need for accuracy in the results.

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