Sampling Bias IELTS Reading Answers

This article provides the Sampling Bias IELTS Reading answers. “Sampling Bias” is a real Reading test passage that appeared in the IELTS exam. With consistent practice, the Reading Module can become your highest-scoring section. To achieve a good score, it is essential to understand how to approach and answer the different question types effectively. 

By solving and reviewing sample reading questions from past IELTS papers, you can strengthen your comprehension skills and boost your accuracy. Try the Sampling Bias practice test below, and explore more IELTS reading practice tests from IELTSMaterial.com to improve further.

Sampling Bias IELTS Reading Passage

1. Our primitive ancestors left many paintings on the walls inside caves. Additionally, inside and near these places there is evidence of re pits, and refuse and burial sites. However, one could equally imagine this same evidence of daily life on exposed cliffs or hillsides, on trees or animal skins, and beside rivers and coastlines. Such evidence, if it existed, would have long been washed, eroded, or rotted away. Thus, prehistoric people are characterized as ‘cavemen’, presumed to have a predilection for dwelling in these places only because that is where most evidence is taken. This ‘caveman effect’ is an example of what is known as ‘sampling bias’ — one of the biggest problems when conducting any form of statistical data gathering.
2. Surveys, for example, are popular because they are easy to administer and relatively costeffective, particularly if conducted remotely through technical means, such as telephone, mail, email, or the Internet. Surveys also lend themselves to obtaining particularly large numbers of respondents, which, in theory, allows a greater chance of sampling all the variations of the target population. They can also be standardised with xed questions and responses (such as ‘tick the box’ or ‘closed-ended' questions). This allows easy collation, analysis, and presentation of results, all with the air of precision that mathematics brings. Such surveys, however, have proven notoriously unreliable because of the difculty in obtaining representative samples. In other words, the sampling is biased, or skewed in favour of certain outcomes.
3. Let us look at some examples. If one calls people on cellphones, it immediately excludes those who favour landlines, and thus the sample of respondents may be those who are more technically-conversant, skewing data based on, say, technical issues (‘How often do you use the Internet?’). If one rings domestic homes during the daytime, most of those who work during the day will be excluded. Those that answer will more likely be the unemployed, disabled, elderly, and retired, skewing data based on, say, work-related issues (‘How important is work in your life?’). No matter how large the sampling size is, sampling bias can immediately invalidate the results.
   One of the more subtle of sampling biases is known as self-selection. No matter how rigorously the respondents are chosen to be random and characteristic of the target population, those who choose to respond will be different to those who do not. Generally, respondents who are willing to invest time in giving answers obviously want to say something, whereas those who choose not to answer probably do not. Thus, any survey in which many respondents do not answer, do not give clear answers, or only give cursory or unthinking answers, is immediately invalidated, since opinionated perspectives are disproportionately represented.
4. The latter is such an immediate and obvious problem that it has given rise to techniques to maximise the possibility of garnering responses. One of the more effective is to give the respondents advanced warning (often through the mail), highlighting the time, the nature of the survey, and the mode of delivery, as well as expressing appreciation for the assistance. The interviewers themselves must be sufficiently trained in correct question-asking techniques, and, with cranks, salespeople, and scam-artists abounding, interviewers must provide introductions about themselves, their company, and the nature of the interview, fully and with evident sincerity, in order to gain the trust of those they are talking to.
5. Even with this, sampling bias can easily arise due to the number of variables in place, since it only takes one to skew the data. If taking samples from a specic location — say, a street corner—then it may be that this location is in the business district, excluding ordinary workers from the sample. It may be that it is near a restaurant district, excluding those who cook more often for themselves. If there is a health club nearby, the majority of respondents may be much healthier than the average of the population. If it is on a university campus, designed to poll university students, is it near the engineering or the arts faculty? The part-time or full-time schools? Are they rich or poor? Male or female? What about race, colour, gender, religion, socio-economic background, and first language? The list goes on and on.
6. One method to deal with this is to make sure all targeted groups are represented, if only a little, and make mathematical extrapolations to correct the bias. For this to work, the degree of underrepresentation needs to be quantied exactly, and one needs to assume the underrepresented respondents are indeed typical of their kind. If, for example, one aims to nd the opinion of the population regarding the outcome of an election, but could only, for whatever reasons, interview one woman for every four men, the responses of the women could be multiplied by four, and thus, one can assume (guardedly and with many provisos), that the sampling bias from gender has been corrected. But that does assume all the other variables which introduce bias have been excluded — often a very problematic assumption to make.

Sampling Bias IELTS Reading Questions

Questions 1-5

Write an answer in boxes from 1-5 by using:

• True:           if the statement matches with the writer's point of view
• False:          if the opinion of the writer contradicts the statement
• Not Given:  if there is no information about the statement

1. Cavemen were often very good artists.
2. Surveys can be done cheaply by telephone.
3. Surveys can usually give reliable information.
4. The elderly and disabled people are often at home during the day.
5. Larger survey samples can reduce sampling bias.

Questions from 6-11

Write answers using no more than two words in the answer box given from 6-11

6.________ Sampling bias
7. _____ are over-represented
8. need to _____ number of responses.
9. ensure interviewers are ________
10. give complete and honest ___
11. to build ___

Questions from 12-13:

Choose the best suitable answer from the options given below each statement. Write the answer in the box from 12-13

12. The number of sampling variables

1. is usually not so large.
2. can result in important input being lost.
3. means many locations need to be used.
4. can result in lists being necessary.

13. Mathematical extrapolation

1. can yield confident results.
2. requires responses from both men and women.
3. needs exact ratios.
4. needs many respondents.

Sampling Bias IELTS Reading Answers

Let’s now review the answers to the questions from the passage in the reading section, Sampling Bias IELTS Reading Answers, and assess your improvement for a high IELTS Reading band score.

Tips to Ace Sampling Bias IELTS Reading Answers

Let us check out some quick IELTS Exam Preparation Tips for Band Score of 8+ to answer the types of questions in the Reading Answers. 

True/False/Not Given (T/F/NG)

• Read carefully – focus on the exact meaning of the statement; small changes can alter the truth.
• Look for keywords and synonyms in the passage; IELTS often paraphrases information.
• Avoid assumptions – do not add information that isn’t in the passage.
• Check every part – if only part of the statement is correct, it is FALSE or NOT GIVEN.
• Watch for absolutes – words like all, always, never often indicate a FALSE answer.

Sentence Completion

• Identify keywords in the sentence and scan the passage for their synonyms.
• Pay attention to grammar – the answer must fit grammatically in the sentence.
• Check the word limit as instructed (usually 1–3 words).
• Read a few words before and after the blank in the passage for context.
• Avoid adding words not found in the passage; answers must come directly from it.

Multiple Choice Questions (MCQ)

• Read the question stem carefully to understand what is being asked.
• Underline keywords in both the question and passage.
• Scan for paraphrasing – the correct option is rarely word-for-word from the text.
• Eliminate obviously wrong options to narrow your choices.
• Be careful with distractors – some options may be partially correct but not fully supported by the passage.

Mastering passages like Sampling Bias IELTS Reading Answers requires careful attention to keywords, paraphrasing, and logical connections in the text. Using this guide’s answers, explanations, and tips, you can strengthen your reading strategies, boost accuracy, and enhance your overall IELTS Reading performance. Keep practising with more IELTS Reading Recent Actual Tests  and answers on IELTSMaterial.com to improve your speed, accuracy, and overall performance.

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