Population vs. Sample: How to Justify Your Participant Choice (Chapter 3)

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Population and sample are two words doctoral students mix up all the time and your committee will notice. Get this distinction wrong, and your entire methodology feels shaky, even if the rest of your writing is excellent. Get it right early, and you'll save yourself months of rewrites and the occasional snide comment from your committee.

In this video, Dr. Steve Tippins — a professor with 35+ years of experience, earned tenure twice, published 60+ papers, and sat on 300+ dissertation committees. Today, as founder of Beyond PhD Coaching, he's helped hundreds of students graduate with their doctorates, succeed in their careers, and use their expertise to change the world for the better. See case studies: https://www.beyondphdcoaching.com

Drawing from decades of experience in academia, Dr. Tippins walks you through the key distinctions between population and sample, how sampling works differently in quantitative vs. qualitative research, and the most common ways students accidentally bias their study without realizing it.

You'll learn:
✅ How sampling works differently in quantitative research (G*Power, sample size, generalizability) vs. qualitative research (data saturation, smaller samples, depth over breadth)
✅ Sampling error and bias: why your method of reaching participants can quietly ruin your study
✅ The sampling types doctoral students actually use: random, purposive, and snowball — with clear warnings about each
✅ A quick pre-submission checklist: population, sample, access plan, bias check, and size justification

0:00 Introduction: Why Population vs. Sample Matters
0:43 Population = Whole Group, Sample = Subset
1:07 The Pie Analogy
1:26 Why Do We Use Samples?
1:56 Sampling Saves Time and Money
2:30 Samples Allow Us to Infer Results
2:56 Sampling Happens Everywhere
3:12 The Goal of Sampling Is Representation
3:47 Does Your Sample Represent the Population?
4:09 Generalizability Depends on Adequate Sampling
4:31 Watch Out for Biased Samples
4:58 Qualitative Studies Are Not Usually Generalizable
5:36 Qualitative Provides Depth, Quantitative Tests Hypotheses
5:52 Qualitative Studies Can Lead to Quantitative Research
6:07 Sample Size Must Be Sufficient (G*Power Test)
6:29 Bias and Sampling Errors
7:01 Examples of Sampling Bias
7:29 If Part of Your Population Can't Be Reached, You Have Bias
7:33 Common Sampling Techniques
7:42 Random Sampling
8:05 Purposive Sampling
8:40 Snowball Sampling
9:07 Don't Mix Sampling Methods
9:25 Explain Population and Sample in One Sentence Each
9:51 Explain How You'll Access Participants
10:01 Acknowledge and Address Bias
10:16 Justify Sample Size with G*Power (Quantitative)
10:25 "10 Participants or Data Saturation, Whichever Comes First"
11:00 Final Thoughts and Resources

If you're working on your methodology and want to make sure your population and sample are airtight before your committee sees them, this video gives you everything you need to get it right the first time.

Book a Free Discovery Call: https://www.beyondphdcoaching.com
Connect on LinkedIn: https://www.linkedin.com/in/steve-tippins

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Видео Population vs. Sample: How to Justify Your Participant Choice (Chapter 3) канала Beyond PhD Coaching