Q.Explain why "editing of data" is a necessary step after data has been collected, and mention the three things an investigator checks for during editing.

Raw collected data can contain blanks, contradictions, or implausible figures; editing catches these before the data is used for classification or analysis. …

Step 1 — Explain why editing is necessary. Data collected through interviews, schedules, or questionnaires can easily contain blank/missing answers, answers that contradict each other within the same respondent's form, or figures that are clearly implausible (e.g., a reported age of 250 years, likely a recording slip). If such raw data is passed straight into classification and analysis (the next stages of a statistical investigation) without being checked, these errors would silently distort every mean, percentage, or conclusion computed from it later — exactly the "garbage in, garbage out" risk this chapter opens with.

Step 2 — State the three checks performed during editing. (a) Completeness — no important item has been left blank or unanswered; (b) Consistency — the answers given do not logically contradict one another (e.g., a respondent's stated age should be consistent with their stated year of birth); (c) Accuracy — the values recorded are within plausible, reasonable limits for what they represent. …

Confusing "editing of data" with "classification of data" (the next chapter's topic, which groups and arranges already-clean data) is a common mix-up — editing is strictly the quality-check …