Speaker 1: Welcome back everybody.
Speaker 2: Today, we're going to be looking at statistical concepts. So
effective research design psychology requires unligning the chosen methodology with
a specific research question while acknowledging inherent limitations. Fundamental questions
to guide design selection include what is the research question?
Speaker 1: What is feasible? What is ethical? What is valid?
Speaker 2: So here's some of the concepts you need to know.
Central tendency which refers to measures that describe the typical
representative score in a data set, typically the mean, the.
Speaker 1: Median, or the mode.
Speaker 2: Variability quantifies express or dispersion of scores around the central value,
assessed through metrics such as standard deviation, varians, and range
distributions characterized by skewness, asymmetry and critosis, which is pikedness
with the normal distribution serving as the reference curve from any.
Speaker 1: Infra ritual procedures.
Speaker 2: Hypothesis testing involves comparing a null hypothesis it's an alternative hypothesis.
Will expand on these topics later. Folks relying on p values,
alpha levels.
Speaker 1: And statistical power.
Speaker 2: Errors and testing encompass type one errors, false positives and type.
Speaker 1: Two false negatives.
Speaker 2: Effect sizes like Cohen's d R squared or N squared
indicates some magnitude and practical meaningfulness of a finding beyond
mere statistic significance. Confidence intervals provide a range estimate for
the true population parameter, offering greater insight than p values alone.
Statistics must inform research designs, sample sized planning, and result
interpretation within context. So let's take a look the measures
again of central tendency. This is under the category descriptive statistics.
The mean is the arithmetic average calculated by summing all
scores and dividing by the number of observations.
Speaker 1: It is sensitive to.
Speaker 2: Extreme values or outliers, which can distort it in skew distributions.
Speaker 1: The middle score in a data.
Speaker 2: Sete from lowest to highest.
Speaker 1: It is robust to outliers and.
Speaker 2: Particularly useful with skewed data or ordinal categorical measures. Mode
is the most frequently occurring score. It is helpful for
categorical data and can identify multiple peaks in multimodal distributions.
Speaker 1: Measures of variability, they're three we mentioned.
Speaker 2: Range is the difference between the highest and lowest score.
It is simple but quite unstable as it relies solely
on two values.
Speaker 1: The variance is the average square difference from the mean.
Speaker 2: It quantifies overall dispersions.
Speaker 1: The next is standard deviation, the square root of the variants.
Speaker 2: It is expressed in the original units of measurement and
indicates how far.
Speaker 1: Scores typically deviate from the mean.
Speaker 2: Approximately about sixty eight percent of scores fall within one
plus or minus standard deviation and a normal distribution. Speaking
of distributions, we'll look at two different types. The skewness
is describing the asymmetry. A positive skew, which skews to
the right, features a tail extending to the right, so
things like income distributions.
Speaker 1: A negative skew was left skew features a tail to
the left.
Speaker 2: Certain test scores with sealing effects cretosis. It describes peakedness
and tail heaviness. Relative to a normal distribution. You have
what they call a leptocurtic distribution, which are tall and skinny,
more scores at extremes planet curtic, which are flat fewer extremes.
The normal distribution is called mesocurtic.
Speaker 1: If you're curious.
Speaker 2: It serves as a benchmark from many statistical tests. You'll
see the bell curve in the normal distribution. Inferential statistics
and hypothesis testing. Inferential statistics allow researchers to draw conclusions
about populations from sample data.
Speaker 1: The null hypothesis.
Speaker 2: That's H with a zero states there is no difference
or no effect. Alternative hypothesis H with a one st
there is a difference or effect. Research designs aim to
disprove the null rather than directly proving the alternative. The
P value is the probability of observing the obtained results
or more extreme, assuming the null hypothesis is true. If
the p is less than alpha, commonly points zero five,
the null is rejected, indicating a five percent risk of
type one error. Type one error means rejecting a true
null hypothesis false positive, so in other words, you're saying
the no hypothesis isn't true. The probability of observing the
obtained results, assuming the null hypothesis true, again as the
P value.
Speaker 1: If p alpha p value, the null is rejected.
Speaker 2: If it's less than point zero five, indicating a five
percent risk of type one errors Type two errors is
feeling to rejecting.
Speaker 1: False null hypothesis, So false.
Speaker 2: Negative statistical power represents the probability of detecting a true effect.
Researchers typically aimed for power greater than point eight zero,
which increases with larger sample sizes. Effect size quantifies the
strength of an observe relationship or difference. One type of
measurement is Cohen's d standard mean difference between two groups.
Zero point two equals small, zero point five is medium,
and zero point eight is large. R squared is coefficient
of determination. It's looking for the meaningfulness the proportion of
variants explained by a predictor and squared a ETA squared,
as they call it. Ata used in a nova indicates
proportion of variants accounted for by the independent variable. Confidence
intervals a arranged likely to contain the true population parameter,
So think of ninety five percent confidence interval. Narrow Narrower
intervals reflect greater precision. Unlike P values, confidence intervals can
be both direction and magnitude. In some say it's more
important than the P value. Connecting statistics to research design,
sample size planning via power analysis is essential prior to
data collections. Smaller samples increase error and reduce power. They
elevate the type two error risk. Design choices such as
within subjects, have higher sensitivity, so lower type two errors
fewer participants versus between subjects, which requires more participants but
avoids carryover effects.
Speaker 1: These influence statistical outcomes.
Speaker 2: Researchers must control confounds through randomization, matching or statistical adjustment.
When interpreting results, avoid over reliance on significance. Instead evaluate
effect strength, contextual meaningfulness, generalizability, and study limitations.
Speaker 1: Let me give you an example APA style reporting.
Speaker 2: Participants in the CBT group reported significantly fewer panic symptoms.
The mean was four point two, standard deviation is one
point one. Then controls the mean is six point seven,
standard deviation one point three, The t is twenty eight
equals three to twenty three. The p value was point
zero one, and the effects size the COENSD was zero
point seven eight, which was what large On that end. Statistics,
when applied thoughtfully, transform raw data into actionable insights that
advance psychological science. Let's give you some examples of some
of the terms we've talked about. So the mean again
is the average. So in a study of anxiety scores
scals zero to forty among therapy clients, a mean of
twenty eight suggest the typical client experience is moderate to
high anxiety, especially if it's based off of the BDI
media and is the middle value and order data. So
an income data for a community mental health of sample
skewed by high earners. The media better represents central income
than the mean. How about the mode well In a
survey of preferred coping strategy, social support being the mode
indicates it is the most frequently endorsed strategy. Standard deviation.
Let's look at this. It's the average deviation from the mean.
So you have depression scores with a mean of fifteen.
Standard deviation of five indicates scores fall between ten and
twenty in larger SD or standard deviation signals greater heterogeneity
and symptoms.
Speaker 1: Severity skewness is an example.
Speaker 2: Like the reaction time data and cognitive psychology often shows
positive skew due to occasional very slow responses. Critosis and examples.
Intelligence test scores approximating normal mesocurtic distribution allow standard parametric tests.
No hypothesis. An example would be is no effects. Or
an example would be CBT has no effect on depression
scores compared to weight less control alternatives.
Speaker 1: There is an effect.
Speaker 2: CBT reduces depression scores relative.
Speaker 1: The control type.
Speaker 2: One is a false positive, concluding a new therapy is
effective when it's not potentially leading to widespread adoptions. So
basically saying that the Noull hypothesis has been rejected when
it hasn't. Type two error is false negative failing to
detect it be beneficial effective mindfulness on stress missing an
opportunity for clinical application. So this is saying that Noll
hypothesis is true when it's not. Cohen's D is standardized
effects size point eight five for exposure therapy on PTSD
indicates a large meaningful improvement. Confidence interval range for population parameter.
For instance, a ninety five percent confidence level for mean
difference and treatment outcomes two point one and five point
three suggest the true effect is positive and reasonably precise.
Speaker 1: Here's a practice question for you.
Speaker 2: A researcher finds that a new intervention yields point zero three,
a P value of point zero three, which is statistically
significant with the Coens D of zero point twenty five.
So what is the size of the Cohen's D? If
you said small, you're right. I hopefully this will help
you with today's podcast. If we talked to some course
statistical concepts, we didn't go into too much detail, too severe,
but hopefully you learned a little bit from there.