What is Non-Sampling Error?

S3-SA3-0362

Grade Level:

Class 9

AI/ML, Data Science, Physics, Economics, Cryptography, Computer Science, Engineering

Definition

What is it?

Non-sampling error refers to mistakes that happen during data collection, processing, or analysis, even if you're looking at every single item in a group. These errors are not due to choosing only a part of the group (sampling), but rather human mistakes or problems with the survey design itself.

Simple Example

Quick Example

Imagine your school is counting how many students want to play football. A non-sampling error would be if the teacher accidentally counts one student twice, or forgets to count a student who raised their hand. The mistake isn't because they only asked a few students, but because of a counting error.

Worked Example

Step-by-Step

Let's say a local chai shop wants to know how many customers prefer ginger chai. They decide to ask every customer for one hour.

1. **Survey Design:** The shopkeeper prepares a simple form to tick 'Ginger' or 'Masala' for each customer.
2. **Data Collection:** A new helper is assigned to ask customers. The helper is busy and sometimes forgets to ask, or marks the wrong box by mistake.
3. **Data Entry:** At the end of the hour, the shopkeeper tries to count the ticks. They accidentally misread a 'Ginger' tick as a 'Masala' tick for two customers.
4. **Result:** The shopkeeper concludes 30 customers prefer ginger chai, but actually, 32 customers preferred it. The difference of 2 is a non-sampling error due to collection (forgetting to ask) and processing (misreading).

Answer: The non-sampling error in this case led to an incorrect count of 30 instead of 32 for ginger chai preferences.

Why It Matters

Understanding non-sampling errors is crucial in fields like Data Science and AI/ML, as bad data leads to bad predictions. In Economics, it ensures accurate policy decisions, and in Engineering, it helps build reliable systems. Data analysts and researchers constantly work to minimize these errors for better insights.

Common Mistakes

MISTAKE: Thinking non-sampling error only happens when you don't collect data from everyone. | CORRECTION: Non-sampling error can happen even if you survey *everyone* in a population. It's about mistakes in how data is gathered or handled, not about who you select.

MISTAKE: Confusing non-sampling error with sampling error. | CORRECTION: Sampling error happens because you only survey a *sample* of the population, so your sample might not perfectly represent the whole. Non-sampling error is about *mistakes* in the process itself (like wrong questions or miscounting), regardless of whether you sampled or surveyed everyone.

MISTAKE: Believing non-sampling errors are always accidental. | CORRECTION: While many are accidental, non-sampling errors can also arise from poorly designed questions (e.g., leading questions) or respondents intentionally giving wrong answers (e.g., privacy concerns).

Practice Questions

Try It Yourself

QUESTION: A survey asks people about their favorite mobile app. If a person misunderstands the question and answers about their favorite TV show, what type of error is this? | ANSWER: Non-sampling error (specifically, a response error due to misunderstanding).

QUESTION: A census (which tries to count everyone) reports the total number of households in a city. Later, it's found that 50 houses in a remote area were completely missed by the enumerators. Is this a sampling error or a non-sampling error? Explain. | ANSWER: This is a non-sampling error. It's an error of 'coverage' – some parts of the population were missed, not because of sampling, but due to a flaw in the data collection process.

QUESTION: A small shop owner counts all the customers who bought a specific snack item in a day. At the end of the day, they manually enter the counts into a spreadsheet. If they accidentally type '25' instead of '35' for one hour's sales, and later realize some customers were not asked if they bought the snack, identify two types of non-sampling errors that occurred. | ANSWER: Two types of non-sampling errors are: 1) Data entry error (typing 25 instead of 35) and 2) Non-response error (some customers were not asked, leading to missing data).

MCQ

Quick Quiz

Which of the following is an example of a non-sampling error?

The average height of 100 students is slightly different from the average height of all 1000 students in the school.

A survey question about income makes people uncomfortable, so some provide incorrect answers.

A small group of students is chosen to represent the entire class for a survey.

The results of a poll vary slightly each time a new random group is selected.

The Correct Answer Is:

Option B describes a 'response error' where participants give incorrect information, which is a type of non-sampling error. Options A, C, and D all relate to sampling error or the nature of sampling itself, not mistakes in the data collection or processing.

Real World Connection

In the Real World

When ISRO launches a satellite, every piece of data from its sensors is critical. If a sensor gives faulty readings due to a calibration issue (a type of measurement error), that's a non-sampling error. Similarly, when app companies like Zomato collect feedback, if their survey form has confusing questions, it can lead to non-sampling errors that affect their understanding of customer satisfaction.

Key Vocabulary

Key Terms

BIAS: A systematic error in a study that leads to an incorrect result | DATA COLLECTION: The process of gathering information | DATA PROCESSING: Organizing and structuring collected data for analysis | RESPONSE ERROR: When a respondent gives an incorrect or misleading answer | COVERAGE ERROR: When certain parts of the population are missed or double-counted

What's Next

What to Learn Next

Now that you understand non-sampling error, you should explore 'Sampling Error'. Learning about both will give you a complete picture of why survey results might not always be perfectly accurate and how to make better sense of data in the real world!