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<v Speaker 1>Welcome back everyone.

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<v Speaker 2>Today, we're going to be looking at a lot of

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<v Speaker 2>more statistics here in the next few podcasts, and then

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<v Speaker 2>we're going to get into legal and ethical issues.

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<v Speaker 1>Sampling is the one we're going to be talking about today.

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<v Speaker 2>Before you analyze anything, you must run any single statistic.

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<v Speaker 2>You have to decide who gets included in your study,

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<v Speaker 2>and that decision shapes everything. Here's the key idea that

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<v Speaker 2>each tripop wants you to hold. Choose the wrong sample

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<v Speaker 2>and even perfect statistics. When this leads you, you can

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<v Speaker 2>run the most sophisticated regression in the world. But if

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<v Speaker 2>your sample doesn't represent the population you care about, your

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<v Speaker 2>conclusions are built on sand. So when you're reading a study,

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<v Speaker 2>you need to ask yourself who was sampled, how were

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<v Speaker 2>they recruited, does this sample reflect the target population? And

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<v Speaker 2>are there demographic or cultural groups that limit where we

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<v Speaker 2>can conclude that last question. Matt is enormously in clinical

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<v Speaker 2>psych where so much foundational research was conducted on narrow

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<v Speaker 2>Western educated, industrialized, rich, and democratic ample. If a therapy

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<v Speaker 2>works in that population, we cannot assume it generalizes to

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<v Speaker 2>an unhoused individual. That bridge from study to real world

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<v Speaker 2>application is called external validity. We've covered this before, so

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<v Speaker 2>probability samplings. Our first one means every individual the population

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<v Speaker 2>has a known and non zero chance of being selected.

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<v Speaker 2>This is what supports generalizability, and there are four types

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<v Speaker 2>of probability sampling. Simple random, every person in the population

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<v Speaker 2>has an equal chance of being chosen.

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<v Speaker 1>Think of it like drawing names out of a hat.

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<v Speaker 2>You need a complete population list to do this, which

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<v Speaker 2>is often impractical for large populations, but when you can

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<v Speaker 2>pull it off, it's the gold standard. Imagine you're studying

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<v Speaker 2>depression rates among the licensed psychologists. If you have access

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<v Speaker 2>to the full board list and randomly seared names from it,

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<v Speaker 2>that's simple random sampling every licensed psychologist an equal shot.

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<v Speaker 1>Stratified divide the.

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<v Speaker 2>Population into subgroups called strata, things like gender, ethnicity, or

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<v Speaker 2>diagnostic category, and then randomly sample.

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<v Speaker 1>Within each group.

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<v Speaker 2>The goal is ensuring proportional representation across key demographics. So

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<v Speaker 2>you're studying CBT outcomes across racial groups. If you just

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<v Speaker 2>randomly sampled from a clinic, you might end up with

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<v Speaker 2>a sample it's eighty five percent white because that reflects

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<v Speaker 2>clinic demographics. Stratified sampling insures you have adequate representation from Black, Latino,

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<v Speaker 2>and Asian clients, making your finds more clinically meaningful and generalizable.

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<v Speaker 2>Cluster sampling, instead of sampling individuals, you randomly select entire

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<v Speaker 2>groups like schools, hospitals, or neighborhoods, and then same individuals

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<v Speaker 2>within those clusters. This is efficient for geographically spread populations,

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<v Speaker 2>but it produces more sampling error than stratified sampling because

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<v Speaker 2>of group level variants, people within the same cluster tend

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<v Speaker 2>to be more similar to each other the broader population.

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<v Speaker 1>You want to.

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<v Speaker 2>Study anxiety prevalence in community mental health centers across a

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<v Speaker 2>large state, rather than sampling every center, you randomly select

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<v Speaker 2>ten and access all clients within them.

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<v Speaker 1>That's cluster sampling.

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<v Speaker 2>Systematic sampling you select every individual from a list after

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<v Speaker 2>a random starting point. So if you look at if

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<v Speaker 2>K equals ten, you start at a random number, say

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<v Speaker 2>number four, then take number fourteen, twenty four, thirty four,

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<v Speaker 2>and so forth. It's easy to implement, but it carries

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<v Speaker 2>one specific risk, what they call periodicity bias. If there

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<v Speaker 2>is a repeating pattern in the list that aligns with

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<v Speaker 2>your sampling interval, you wire systematically over under select certain

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<v Speaker 2>types of people. Example, would be you have a list

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<v Speaker 2>of therapy and take appointments organized by day of week.

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<v Speaker 2>If you take every seventh appointment and the list cycles weekly,

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<v Speaker 2>you might only ever select clients who come in on Mondays,

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<v Speaker 2>potentially missing weekend to only available clients who may differ

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<v Speaker 2>in employments, status, or severity. Another one now is non

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<v Speaker 2>probability sampling. We looked at probability sampling methods that included

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<v Speaker 2>systematic sampling, cluster sampling, stratified, and simple random. Now we

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<v Speaker 2>look at non probability and it's used when probability sampling

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<v Speaker 2>isn't feasible. The trade off is that generalizability is limited.

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<v Speaker 2>You have to know that's going into your interpretation. The

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<v Speaker 2>first one is convenience. Sampling participants are selected based on

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<v Speaker 2>availability or proximity. Fast, low cost, easy to do, and

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<v Speaker 2>highly vulnerable to selection bias. The classic examples recruiting undergrad

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<v Speaker 2>psych students because they're sitting in the building common in

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<v Speaker 2>academic research, but you have to be cautious about what

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<v Speaker 2>you can claim from those findings. Clinical example, a researcher

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<v Speaker 2>studies the effect of mindfulness on stress by recruiting from

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<v Speaker 2>a university psychology department waiting list. These are people who

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<v Speaker 2>already sought help, are likely college educated, are probably not

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<v Speaker 2>representative of community health mental health populations. The researcher deliberately

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<v Speaker 2>selects participants who meets specific criteria. This is especially useful

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<v Speaker 2>in qualitative research. This is purpose of sampling. So again,

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<v Speaker 2>the researcher deliberately selects participants who meets specific criteria. This

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<v Speaker 2>is especially useful in qualitative research. For instance, you're studying

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<v Speaker 2>the lived experience of psychologist who have treated clients with

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<v Speaker 2>Capgras syndrome, so you purposely selected or recruited clinicians we

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<v Speaker 2>have direct treatment experience. Snowball sampling, existing participants recruit future

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<v Speaker 2>participants from their social networks. This is used when you're

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<v Speaker 2>studying hidden or stigmatized populations where a sampling frame doesn't exist.

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<v Speaker 2>It's hard to get individuals like distress and undocumented immigrants

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<v Speaker 2>who are unlikely to respond to traditional recruitment. You start

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<v Speaker 2>with two or three participants through a community organization and

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<v Speaker 2>ask them to refer other as they trust, and then

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<v Speaker 2>it grows.

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<v Speaker 1>Lastly, it's quota sampling. These are like.

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<v Speaker 2>Stratified sampling and structure, but non random and execution. You

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<v Speaker 2>identify categories you want represented and feel predetermined quotas for

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<v Speaker 2>each is fast and structured, but it lacks true randomness,

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<v Speaker 2>so you can't apply probability based statistical inferences.

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<v Speaker 1>The same way.

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<v Speaker 2>Another area sampling distributions, the central limit theorem and standard error.

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<v Speaker 2>Even good samples vary from the population, there's always some

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<v Speaker 2>degree of sampling error. But here's the insight. If we

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<v Speaker 2>understand how samples behave across many repetitions, we can estimate

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<v Speaker 2>population parameters from a single sample. That's the whole game.

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<v Speaker 2>This is the theoretical distribution of a statistics, say the

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<v Speaker 2>mean across many random samples drawn from the same population.

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<v Speaker 2>You're not looking at one sample. You're imagining what the

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<v Speaker 2>distribution of that statistic would look like if you do

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<v Speaker 2>samples over and over again. This is one of the

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<v Speaker 2>most important theorems in all of statistics, and the e

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<v Speaker 2>triple p tested directly. Here's what it says for the

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<v Speaker 2>central limit theorem. With a large enough sample, typically thirty

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<v Speaker 2>or more, the sampling distribution of the mean becomes approximately normal,

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<v Speaker 2>regardless of the shape of the population distribution. Why doesn't

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<v Speaker 2>matter because it justifies using what we call parametric tests

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<v Speaker 2>even when your underlying population isn't normally distributed.

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<v Speaker 1>If you're studying.

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<v Speaker 2>Trauma scores in a community sample and a distribution is scored,

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<v Speaker 2>you can still use a T test or a NOVA

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<v Speaker 2>if your sample is large enough, because the central limit

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<v Speaker 2>theorem tells you the sampling.

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<v Speaker 1>Distribution of the mean will be normal. Standard error this.

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<v Speaker 2>Is the standard deviation of the sampling distribution. It tells

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<v Speaker 2>you how much sample means vary from sample to sample.

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<v Speaker 2>The formula's se equals the standard deviation divided by the

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<v Speaker 2>square root of N. An example would be a sample

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<v Speaker 2>sized increases standard error decreases, your estimate of the population

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<v Speaker 2>mean becomes more precise. This is why large critical trials

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<v Speaker 2>are more trustworthy than small pilot studies, not because the

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<v Speaker 2>researchers are smarter, because the math of sampling gives them

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<v Speaker 2>a more stable estimate. There's five factors that affect sampling size.

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<v Speaker 2>First is the effects size. This is the magnitude of

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<v Speaker 2>the difference or relationship you expect to find. Cohen's benchmarks

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<v Speaker 2>are d equals zero point two zero for small, zero

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<v Speaker 2>point five for medium, and zero point eight for large.

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<v Speaker 2>Smaller effects require larger samples to detect. If you're studying

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<v Speaker 2>a subtle early intervention effect on subclinical anxiety, you need

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<v Speaker 2>more participants than if you're studying the effect of a

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<v Speaker 2>major trauma event on PTSD. Severity power is the probability

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<v Speaker 2>of detecting a real effect when one truly exists. The

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<v Speaker 2>conventional target is zero point eight, meaning on eighty percent

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<v Speaker 2>chance of detecting a true effect. Power increases with larger

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<v Speaker 2>sample size, larger effects size, and higher alpha on the

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<v Speaker 2>h If a study fails to find a significant result

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<v Speaker 2>with low power, you should be suspicious. That might be

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<v Speaker 2>a type two error false negative, not a true null

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<v Speaker 2>result type one error false positive. This is the threshold

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<v Speaker 2>you used to decide whether it's a result is statistically significant,

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<v Speaker 2>also called the alpha level. Alpha level type one error

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<v Speaker 2>rate typically set at zero point zero five. If you

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<v Speaker 2>lower alpha to zero point zero one to be more

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<v Speaker 2>conservative to reduce the risk of false positives, you need

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<v Speaker 2>a much larger sample to maintain your power. There's always

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<v Speaker 2>going to be a trade off design complexity. More groups

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<v Speaker 2>and more predictors mean you need a larger sample. A

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<v Speaker 2>study comparing five treatment conditions requires more participants than a

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<v Speaker 2>two group design. Within subject designs where the same person

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<v Speaker 2>is measured multiple times, typically fewer participants are needed because

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<v Speaker 2>you reduce vary variants by using each person on their

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<v Speaker 2>own control. There are also constraints budget, time, and access

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<v Speaker 2>to participants often limit ideal sample sizes. The researcher studying

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<v Speaker 2>a rare personality sort of and I'm able to recruit

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<v Speaker 2>may not be able to recruit three hundred participants, no

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<v Speaker 2>matter how clean the design is. As section six is

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<v Speaker 2>talk about power analysis, which is the form of process

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<v Speaker 2>used to calculate the minimum sample size and needed to

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<v Speaker 2>detect an effect, and you run it before data collection.

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<v Speaker 2>It's a planning tool, not an afterthought. Effect size using

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<v Speaker 2>Cohen's d f orr. Depending on the design, alpha level

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<v Speaker 2>desired power usually set at zero point eight and a

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<v Speaker 2>number of groups of predictors. The standard software used in

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<v Speaker 2>psychology's g power, which is free and widely used. Failing

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<v Speaker 2>to conduct the power analysis before a study creates two problems. First,

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<v Speaker 2>you might end up underpowered and miss a real effect

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<v Speaker 2>that's a false negative type two error. Second, you might

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<v Speaker 2>over recruit, which waste resources and raises unnecessary ethical concerns

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<v Speaker 2>about participant burden. For instance, the researcher wants to study

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<v Speaker 2>whether trauma focus CBT reduces PTSD symptoms more than supportive counseling.

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<v Speaker 2>They expect the medium effects size of D equals zero

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<v Speaker 2>point five. They set alpha zero point five and zero

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<v Speaker 2>point zero five and one power of zero point eight.

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<v Speaker 2>Running g power tells them they need approximately sixty four

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<v Speaker 2>participants per group, so one hundred and twenty eight total.

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<v Speaker 2>If they only recruit forty, they're severely underpowered, and a

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<v Speaker 2>null result tells us almost nothing. So let's talk about

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<v Speaker 2>what goes wrong. Sampling bias is introduced when some members

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<v Speaker 2>of the population are systematically less likely to be included

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<v Speaker 2>in the sample. The key word is systematically self selection bias.

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<v Speaker 2>Volunteers differ from non volunteers in ways that are often

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<v Speaker 2>clinically relevant.

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<v Speaker 1>People who volunteer for.

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<v Speaker 2>Depression treatment studies tend to be more motivated, more resourced,

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<v Speaker 2>and less severely impaired than the average with depression. If

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<v Speaker 2>your results are based entirely on volunteers, you may not

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<v Speaker 2>be systematically overestimating, or you may be systematically overestimating treatment effectiveness.

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<v Speaker 1>Non response by certain groups.

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<v Speaker 2>Are less likely to complete surveys or follow ups if

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<v Speaker 2>clients with the most severe pathology drop out of your

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<v Speaker 2>longitudinal study because they're too disregulated to complete it. Your

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<v Speaker 2>data on chronic compairment is compromised. Undercoverage, certain populations are

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<v Speaker 2>left out entirely. Classic example, unhoused individuals. If you're studying

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<v Speaker 2>mental health outcomes in a city and your sampling frame

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<v Speaker 2>is a clinic registry, you've already excluded the population most

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<v Speaker 2>likely to have severe untreated comobid conditions. Selection effects are

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<v Speaker 2>related but distinct concept. They occur when the process of

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<v Speaker 2>assigning participants to conditions or groups is not random. This

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<v Speaker 2>affects internal validity, your ability to claim that the independent variable,

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<v Speaker 2>not some pre existing difference between groups.

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<v Speaker 1>Cause the outcome.

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<v Speaker 2>Selection effects are why randomized control trials are the gold standard.

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<v Speaker 2>Random assignment breaks the link between pre existing characteristics and

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<v Speaker 2>group measurement membership. Poor sampling equals limited to not generalizability

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<v Speaker 2>external validity, which is the degree to which fundings from

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<v Speaker 2>a study can have generalize to other people. Sampling is

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<v Speaker 2>the primary drive driver of external validity. If your sample

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<v Speaker 2>is narrow, your conclusions are narrow. When reading research like

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<v Speaker 2>a scientist, run through the checklist. Was the sampling method

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<v Speaker 2>is described. Was randomization used? Was the sample diverse and representative?

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<v Speaker 2>How large was it it was? A power analysis reported

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<v Speaker 2>whether high attrition rates are missing data. These are not

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<v Speaker 2>just academic questions and clinical practice that determine whether the

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<v Speaker 2>intervention you're considering for your client was actually tested on

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<v Speaker 2>someone like your client.

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<v Speaker 1>That's it for now, folks,