Difference Between Random Sampling vs Non-random Sampling: Definitions, Distinctions and Examples

1. Basis of Selection: Probability-based vs. researcher-driven.

2. Representation: More representative vs. potentially skewed.

3. Bias Level: Lower systematic bias vs. higher risk of bias.

This question typically refers to how statistical methods handle data from random (probability) vs. non-random (non-probability) samples:

• Random Statistics (from Random Samples):
  

• Uses probability theory to make inferences.

• Confidence intervals and margin of error are valid because each sample element had a known chance of selection.

• Generates generalisable (population-level) insights.

• Non-random Statistics (from Non-random Samples):
  

• Not purely grounded in probability, so inferences are more limited.

• Traditional confidence intervals or margins of error may not apply.

• Results often represent only the subgroup studied, not the entire population.

This question appears repetitive, possibly due to a typographical or query error. A “random sample” is the same as any other “random sample.” Both phrases refer to selecting participants such that every individual in the population has an equal probability of being chosen. There is no difference between a “random sample” and a “random sample,” as they are the same concept.

While “selection” and “sampling” are often used interchangeably, here’s how they compare:

• Random Selection:
  

• Relies on chance mechanisms (e.g., lottery method, random number tables).

• Ensures unbiased inclusion, with every element having an identical likelihood of being chosen.

• Nonrandom Selection:
  

• Based on subjective or practical methods (e.g., picking the most accessible participants).

• Can lead to selection bias because the researcher’s preferences or circumstances influence who is included.

1. Methodology: Probability-based vs. subjective criteria.

2. Bias Level: Reduced systematic bias vs. elevated potential for bias.

3. Generalisability: High external validity vs. more limited.

4. Sampling Frame: Requires a well-defined sampling frame vs. may not require a full list of the population.

5. Complexity: Generally more planning and resources vs. quicker but less statistically robust.

Non-random (or non-probability) sampling encompasses various methods. The three main types are:

1. Convenience Sampling
   

• Definition: Selecting participants who are easiest or most convenient to access (e.g., students in a classroom).

• Pros: Quick, cost-effective.

• Cons: Highly prone to bias; lacks representativeness.

2. Purposive (Judgmental) Sampling
   

• Definition: Researcher relies on expert judgment or predefined criteria to select participants (e.g., only experts in a specific field).

• Pros: Targets a very specific group, useful for niche or exploratory studies.

• Cons: Subjective selection process increases risk of bias.

3. Quota Sampling
   

• Definition: Researcher ensures certain characteristics or quotas are met (e.g., 50% females, 50% males), but participants within those categories are still chosen non-randomly.

• Pros: Ensures representation of specific segments.

• Cons: Not purely random; within each quota, bias can still occur.

• In random sampling, a larger sample size generally increases accuracy and reduces sampling error.

• In non-random sampling, a larger sample may still not eliminate bias if the selection process is flawed.

Yes, but it’s less common. Qualitative research often seeks in-depth insights from specialised groups, making non-random methods more practical. However, random sampling can still be applied to ensure diversity in qualitative data.

Choosing the wrong method can compromise data validity, generalisability, and the credibility of your findings. Researchers must align the sampling method with their study goals, timeline, resources, and target population.

Yes. Snowball Sampling, for instance, is used when participants refer future participants from their social networks (often in hard-to-reach populations).

While you cannot eliminate it, you can minimise bias by:

• Setting clear selection criteria.

• Diversifying recruitment channels.

• Transparently reporting how participants were chosen.