Random Selection: To ensure the sample is representative of the population, respondents should be chosen randomly rather than selecting a convenient or biased group.
Sample Size: The reliability of the conclusions depends on the number of participants. A small sample may not capture the diversity of the population, leading to skewed results.
Bias Identification: Researchers must actively check if the timing, location, or method of distribution favors one demographic over another.
Spot the Overlap: In exam questions, always check the boundaries of numerical ranges. If a value like '10' can fit into two different boxes, the question is flawed.
Check for Constraints: Look for the absence of a time frame. If a question asks 'How much do you exercise?' without specifying 'per week' or 'per month', it is considered unsuitable.
Identify Leading Bias: Evaluate if the question uses positive or negative adjectives (e.g., 'Don't you agree...') that nudge the respondent toward a specific answer.
Ambiguity Trap: Students often miss subtle ambiguities. For example, 'Do you play sport?' is ambiguous because it doesn't define what counts as 'playing' or 'sport'.
Non-Exhaustive Choices: A common error is providing specific options that don't cover everyone, such as listing only three colors without an 'Other' option.
Inequality Confusion: Using symbols like or can confuse some participants; descriptive ranges or discrete intervals are generally preferred for clarity.
