Modules (1-3)

Question 1

What is the publication process?

it takes __ to ___ years
journals are the __ of science

What’s the process?
How long does it take to complete?

Answer 1

The rigorous process primary research articles have to go through to be published.

• It takes 2-4 years after the research is
  conducted is it published in a journal (i.e.,
  publication lag).
• Technology has reduced the lag, but it still
  is a slow process.
• Journals are the gatekeepers of science:
  there is an editor who decides what is
  published (i.e., a scientist who in their
  spare time is the editor of the journal).

The Process:
1. Do the research, write an article and
submit a manuscript to a journal for
publication.
2. The editor of the journal either:
a. Desk rejects article (i.e., article is rejected
before being sent for review; when the
article doesn’t fit the theme of the journal,
there is something fundamentally wrong
with it or it’s not interesting enough).
b. The article is sent out to well-known
researchers who specialize in the topic or
methods used in the target article for 3-4
researchers to critically evaluate it and
provide feedback.
3. The editor takes into account the
feedback provided and comes to a
decision about the article:
a. Accept.
b. Accept under the condition the author
makes small revisions to the article.
c. Revise and re-submit where the author
makes the necessary changes and then
resubmits their article to the journal where
it is sent out again to 3-4 researchers for
review (this process can happen multiple
times till it meets the standards of the
journal).
d. Rejected and author needs to submit the
paper to another journal with lower
standards.

*This process of revision takes between 6-
months to a year and a half to complete.

Question 2

how can we identify a good research article?

Answer 2

the number of citations or the quality of the research journal it was published in.

Question 3

what is a primary source?

Answer 3

Primary Research Literature:
- Articles on research that’s been conducted
by scientists in labs and published in an
academic journal.
- Publication is a key part of scientist’s job, it
determines promotions, grants, good PHD
students and if their students get
scholarships (incentives) but the key reason
is because they love doing it.

Question 4

Not the last word!
Can a single study answer all the questions?
science is __ and ___

Answer 4

• A single study never answers the question
(i.e., specific to participants or variables
used; to answer questions we need
replication; each study is a single piece in
the puzzle).
• No study is perfect. There is always
limitations due to the trade-offs in
designing a study that is ethical and
practical.
• Scientific literature is cumulative and self-
correcting (to build a body of literature; self-
correcting through replication to support or
contradict existing theory; one study
doesn’t throw out a theory but as more and
more accumulate with similar findings
revisions to theory are made; science is
always progressing; they support theory
but do not PROVE anything).
• You do not get paid to publish, review or
edit research articles. It is just a part of your
job as scientists to contribute to the body
of scientific literature.

Question 5

Two Types of Articles

Peer-reviewed scientific literature

Answer 5

1. Empirical Research
   • Report the results of a research project[s] (more
   than one if combined paper with 3-4 studies
   testing the same specific hypothesis)
   • Their standard format is
   Abstract/Intro/Method/Results/Discussion
2. Review Articles
   • Combine the results of several (many) research
   studies to summarise and draw conclusions.
   • Narrative review (more like a story where the
   author takes bits and pieces from different
   studies to make a point on a psychological
   phenomenon).
   • Systematic review (the author has done very
   careful library work to ensure they’ve
   mentioned EVERY study that’s been published
   on the specific question; nothing left out;
   comprehensive review).
   • Meta-analysis (combine data from quantitative
   studies on the specific question to perform
   analysis on and make a comprehensive
   conclusion on the effect in question).
   *still peer-reviewed
   *commentary on how different studies have
   addressed the same question with different
   samples, methods, variables, analysis and
   comment on any consistent patterns found.

Question 6

Is it any good?

Peer-Reviewed Journal:

Answer 6

 Is it in a “good” peer-reviewed journal?
• Use journal rankings to know which journals
have high standards and peer-review.
• You need to watch out for predatory journals
which are journals in it to make money. They
charge authors money to publish studies in
their journal.
 Are conflicts of interest declared?
• Funding? Especially in medical research,
college, intervention/treatment research that
is funded by a company who wants to sell
that treatment if successful.
• Business interests? Financial interests in the
thing they’re writing about.
• These should be declared at the beginning
or the end of the article.
 Read critically yourself
• What are the studies strengths and
weaknesses? How generalisable are the
findings? How does this fit in with other
studies on the same topic? Consistency
across studies increases our confidence that
the findings are reliable and valid.

Question 7

What are secondary sources? Name three examples

Answer 7

Secondary Sources:
People who have read primary literature who have synthesized it into make It readable in a short period of time.

1. Textbooks:
   o A researcher who has knowledge on the
   field and reviewed all the literature on the
   topic to synthesize it and communicate it in
   an easier to digest format.
2. Trade Books:
   o These are books written by researchers on
   their own work/experiences over the years.
3. Media/Journalism Sources
   o Journalism, social media, news outlets,
   blogs etc.
   o Some is good but some of it is very bad
   (sensationalism, misinformation, selling
   something etc.).
   o The media likes to exaggerate and draw
   conclusions that are not supported by
   science. This conflicts with scientist’s
   tendency to draw tentative conclusions that
   are fully supported by science.

Question 8

Is it any good?
Text/Trade Books:

Is it any good?
Media/Journalism Sources:

Answer 8

Is it any good?
Text/Trade Books:

 Is the author also a researcher? Read the
books bio to see if the writer is a
researcher or works at a university.
 Is the author selling something? Do they
have financial interests, an agenda or
lifestyle they’re peddling?
 Who recommends the book? Is it a valid
source like your lecturer, fellow researcher,
librarian or multiple recommendations like
reviews.
 Does it cite sources? Do they have
citations in footnotes or at the end to
support that their claims are based on
scientific literature.
 Is it out of date? Newer up to date
research is because it is cumulative and
self-correcting, so it changes overtime.
 Read critically!

Is it any good?
Media/Journalism Sources:

 Is it a reputable science news source?
 Does it cite the source of the research
(authors, journal)? The best news sources
will provide links or citations to read the
original article.
 Does it get opinion from an independent
scientist? An expert in the field’s opinion.
Small peer-review.
 Does it confuse correlation and causation?
Language. Do they confuse correlation
with causation. Is their type of science
claim valid or supported?
 Does it extrapolate from animals to
humans? Across cultures. Babies to adults
etc.
 Read critically!

Question 9

How do we understand and evaluate primary psychological research?

Answer 9

•	Psychology is a science but,
•	Science is a human activity
•	Like all human activities, science is 
        fallible
•	Errors
•	Biases
•	Chance results
•	Published ≠ proven
•	We must read research critically to 
        identify its strengths and weaknesses.

Question 10

A critical evaluation consists of understanding:

Answer 10

• What was done
• Why it was done
• What was found
• What this means

If evaluation is done properly, any issues/problems with the study will arise naturally while doing the critical evaluation.

Question 11

Critical questions:

Answer 11

1) Research Question
broad and theoretical overview of what
the main question the study aims to
answer.
and, but, therefore
2) Hypothesis/Prediction
construct level operationalized into IV-
DV’s
If…. then.
3) Participants
how many? how were they sampled?
were there criteria for inclusion or
exclusion? what are the implications of
these criteria?
4) Procedure
what was the procedure?
5) Variables
theoretical, operational and type
(between or within-subjects)
6) Design
what type of claim does it make? how
were the variables manipulated?
i.e., mixed 2x factor anova
7) Comparisons
looking at the data, is the pattern within
the data what I expected?
8) Analysis
does the data support my hypothesis?
9) Conclusions
What conclusions are drawn?
Are they justified?
What limitations are identified?
What questions remain unanswered?

Question 12

Four Validities:

Answer 12

1. Construct Validity
    How well do the operational variables map
   onto the theoretical variables?

2. Internal Validity  
 Are there other possible explanations for 
   the findings?
 Are there confounds?
 Is it the best design to address the 
  question?

3. External Validity
    Can the conclusions generalise to other
   people, other stimuli, other contexts?

4. Statistical Validity
 How big is the effect?
 Does the study have sufficient power?
 Is the data treated appropriately?
 Are the statistical conclusions (e.g., 
  significance) justified?

Question 13

Internal Validity allows us to …. and requires…

Answer 13

Internal validity is needed to make causal claims.

Manipulation of IV and observe change in DV to assess temporal association between variables.

Internal validity is to do with how well we achieve control. We will discuss three ways that researchers may fail at establishing interval validity.

Question 14

Three main types of internal validity concerns?

Answer 14

1. Change over time
   - Threats to internal validity which occur as a
   result of time.
2. Selection Effects:
   - These arise when two groups are not
   matched on extraneous variables.
3. Expectancy Effects
   - Two types of expectancy effects:
   Participant vs Experimenter Expectations.

Question 15

(5) change over time threats to internal validity:

Answer 15

(A) Maturation Effects
- People change naturally over time.
(B) History Effects
- Something in the world changes between
pre-test and post-test (not the people)
(C) Mortality Effects
- People drop out of your experiment.
(D) Testing Effects
- Pre-test biases responses to the
manipulation or responses on the post-
test
(E) Order Effects
- The order in which you do the tasks
matters.
- Practice – people get better over time.
- Fatigue – people get tired or bored over
time.

Question 16

(4) Selection Effects threats to internal validity:

Answer 16

2. Selection Effects:
   - Threats to internal validity based on
   participant selection, sampling and
   assignment to conditions.
   - These arise when two groups are not
   matched on extraneous variables.

(A) Self-Selection:
- Participants choose their own
experimental condition
(B) Non-Random Assignment
- People don’t have an equal opportunity
to be in each experimental condition.
(C) Regression to the Mean:
- Any group at the extremes of a
distribution will shift to mean over time.
(D) Small Sample:
- Is unlikely to equate groups on all
extraneous variables

Question 17

Expectancy Effects:

(3) Participant Expectations
(2) Experimenter Expectations

Answer 17

Participant Expectations
(A)	Placebo Effect:
-	Treatment response in patients given 
        an “inert” substance or treatment.
-	Psychological improvement
-	Physiological improvement
-	Influenced by context

(B)	Nocebo:
-	Side effects in participants given an 
        “inert” substance or treatment.
-	Common symptoms
-	Warnings
-	Ethical?

(C)	Demand Characteristics
-	The Good Subject (Orne, 1962)
-	Social Desirability
-	Most research participants want to be 
        seen in a positive light
-	May not be truthful or accurate in 
        admitting negative thoughts/behaviours

Experimenter Expectations:
(A) Experimenter Bias
-	Sometimes experimenters treat 
        participants differently depending on 
        condition.
-	During data collection
-	During data coding

(B) Subjective Coding
- After data is collected the experimenter
can still bias the results.
- Subjective Coding can occur when the
DV requires human judgment (room for
error, bias or expectations)

Question 18

what is a maturation effect?

Answer 18

• People change naturally over time.
• i.e., music lessons which take place
  over 3 months to test causal hypothesis
  that taking music lesson make you
  better at math’s.
• Children naturally get better at math
  overtime, or illnesses go away over
  time, so we naturally expect their scores
  to get better.
• To address this problem, we introduce a
  control condition who does a filler task
  like swimming lessons for the 3 months
  and math scores are taken at time 1 and
  time 2.
• His is good because now we can
  compare people’s natural tendency to
  get better at math overtime (control)
  with people who took music lessons
  (experimental) to see if they improve
  more than the control group.

Question 19

what is a history effect?

Answer 19

• Something in the world changes
  between pre-test and post-test (not the
  people)
• Pandemic or economic crisis which
  occur in the middle of the experiment
• For example, pre & post testing anxiety
  scores to test the effectiveness of
  therapy on reducing anxiety.
• However, therapy during exams or a
  pandemic which will cause fluctuations
  in anxiety independent of the IV
  (therapy) when they come and go
  (contextual factors; changes in the
  world).
• The solution to this problem is a control
  group (does nothing) which measures
  changes in contextual factors on
  anxiety throughout the course of the
  experiment.
• May use a waitlist control group to
  control for time and ensures patients get
  treatment at the end of the study.
• A better control group would be an
  active control group (therapy or self-
  help treatment) to see which treatment
  performs better.

Question 20

what is a mortality effect?

Answer 20

• People drop out of your experiment.
• Problems that arise even when you
  have a good control group!
• Attrition rates (people drop out of the
  study for any reason, non-completers)
• This is a problem when an equal
  number of people fall out of each
  condition (15 vs 2).
• Especially an issue when people drop
  out of therapy; it is hard to do and those
  who drop out may be those who scored
  higher on anxiety.
• Completers and non-completers may
  differ systematically on an extraneous
  variable which makes them more likely
  to drop out than completers.
• To solve this, you can do many things to
  try and keep people in you study (i.e.,
  send reminders of sessions, use
  incentives to increase motivation as
  long as its equal for experimental and
  control group it is a good solution).
• Will need to compare pre-test scores
  between completers and non-
  completers to confirm they’re not
  significantly different and ensure no
  mortality effect is present.

Question 21

What is a testing effect?

Answer 21

• Pre-test biases responses to the manipulation or responses on the post-test
• Measure attitudes towards the environment before and after watching a conservation film (or control film) to test causal effect.
• A testing effect may arise when participants who are asked to rate their attuites towards the environment at the beginning of the experiment will remember what they put and after the film will try to match or increase their score. Participants may try to guess what the study is testing and change their behaviour in what they believe is the desirable for the researcher. This is an issue because the change in DV is due to an extraneous variable and not the IV manipulation.
• Two main effects it has; 1) their performance changes over time due to practice experience with the pretest; 2) they guess the research question and there is a resulting expectancy effect (demand characteristics).
• A solution to this is to:
• Skip the pretest (RA to conservation film or the control film, after the film test their attitudes towards the environment) this assumes that the random assignment will create equivalent groups on environmental attitudes.

Question 22

what is an order effect?

Answer 22

• The order in which you do the tasks matters.
• Practice – people get better over time.
• Fatigue – people get tired or bored over time.
• Manipulate the IV within-subjects design instead of as a between-subjects design (they see both films)
• For example, face inversion effect where we are faster at recognizing upright rather than inverted faces it works as a between subject’s design but would be much easier if we did it as a within subject’s design. In a block of trails inverted than upright faces but now we have issues of order effects. People get better overtime (faster reaction times) or fatigue (reaction times slower) changes in reaction time due to the order materials are presented in and not the level of the IV.
• The solution is to have two within subjects’ experimental groups and have them experience both conditions in a counterbalanced order (A-B, B-A; in blocks). It doesn’t make the practice effects go away but it will balance them out across conditions (i.e., control for them).
• Instead of blocks we can intermix the upright and inverted faces in one block. It doesn’t make the practice effects go away but it effects both inverted and upright faces equally (i.e., control for them).

Question 23

what is self-selection?

Answer 23

• Participants choose their own experimental condition
• Testing the theory that morality is influenced by the time of day. Are people more moral/ethical in the morning than in the afternoon (i.e., loss their self-control overtime).
• People are given an opportunity to cheat in the morning and in the afternoon.
• Participants selected the morning or the afternoon class!!! Some people are morning people, and some people are not; this is a subject factor which influences which class they choose and makes them systematically different (groups).
• Is it morning people are more moral than afternoon people?
• Applies the same for selecting own treatment as well.

Question 24

what is non-random assignment?

Answer 24

• People don’t have an equal opportunity to be in each experimental condition.
• When researchers fail at random assignment.
• People do not have equal opportunity to be in each condition because the researcher assigns people to conditions based on which stream/class they’re enrolled in. It’s not random!
• People who are enrolled in the morning class may be systematically different than the afternoon class. The researcher did not randomly assign people to conditions.
• Multiple classes where ½ use a laptop and ½ use laptop… this equates them over time of day.

Question 25

what is regression to the mean?

Answer 25

• Any group at the extremes of a distribution will shift to mean over time.
• To improve players batting averages he offers those with the lowest average to donate money to charity on every run they score. Their batting average increases with the incentive.
• He tried the same thing with his best players an found that their averages fell. He concludes that they do not like charity. Why is this faulty logic? Regression to the mean, statistical fact, if we select people at the extreme of the distribution and look at them over time they will regress to the mean (worst get better and best get worse). People fluctuate over time on traits but will always come back to the mean/average naturally (compared to self).

Question 26

what is a small sample?

Answer 26

• Is unlikely to equate groups on all extraneous variables
• Two participants (100-120)
• Ten participants (100, 120…)
• The mean will be much higher differences between conditions with smaller sample size relative to larger sample sizes. The more people there are in the group the more likely it will fall into a normal distribution and that we are sampling close to the mean. Smaller sample sizes make us more likely to be distributing from extreme ends of the bell curve and skew the data.
• Random assignment is designed to create equal groups on extraneous variables. Small samples are not likely to be similar to each other even with random assignment!
• How large is large? It depends on how variable about the thing you are measure/control for. A good rule of thumb is 30 people in each group (central limit theorem-normal distribution-sampling from population to get close to the real mean)
• One trick is to sample from people who are already similar with one another.
• How many people does it take to equate groups on anxiety?
  • If you selected from the NZ population?
  o Huge variability.
  • If you selected from first year PSYC?
  o Now I have people with similar interests, education level, stress etc. may not need as many people in each group because there is less variability.
  • If you selected from a group who are prone to anxiety (e.g., family members of people with an anxiety disorder)?
  o Equated genetic risk factor.
  • If you selected from people who have clinical anxiety?
  o Very small level of variability within participants and will need an even smaller sample size.
• How small to go when sampling is a question of having Homogeneous vs heterogeneous samples?
• Do you want a sample very similar that’s homogeneous with a smaller sample size which can make it les representative of the wider population.
• Do you want a variable sample with a larger sample size that is representative of the wider population.
• = external validity.

Question 27

what is a placebo effect?

Answer 27

-	Treatment response in patients given an 
       “inert” substance or treatment.
-	Psychological improvement
-	Physiological improvement
-	Influenced by context

Question 28

What is a nocebo effect?

Answer 28

-	Side effects in participants given an 
        “inert” substance or treatment.
-	Common symptoms
-	Warnings
-	Ethical?

Question 29

what are demand characteristics?

Answer 29

• A series of productivity studies at Hawthorne Electrical works
• Changed lighting, equipment, work schedules, teams etc.
• Most changes led to increased productivity as long as they were noticeable
  ii. Workers building lightbulbs in a factory to test whether organizational changes would increase productivity. They found no matter what they did it always increased work productivity. They were smart and noticed these changes and guessed they were installed to increase productivity, so they changed their behaviour to meet researchers expectations. The change in DV is not due to the IV but researcher expectations
• The Good Subject (Orne, 1962)
• Most research participants aim to please
• Obedient to a fault
• E.g., a hypnosis study, that people weren’t hypnotized but just following instructions. He did a study without hypnosis and gave them an insane task to do, mind numbingly boring and tedious task, they all did as they were told. They respond to the perceived authority of the experimenter. Social desirability effect they want to be viewed in a positive light by the researcher, so they are not as truthful or change their behaviour.
• Social Desirability
• Most research participants want to be seen in a positive light
• May not be truthful or accurate in admitting negative thoughts/behaviours

Solutions:

1. Well designed control condition[s]
2. Participants are blind to condition/treatment
3. Participants are blind to hypothesis
4. Obscuring purpose of methods/scales/measures (deception; filler tasks; confidential/anonymous)
5. Manipulation checks (test after study if they knew the hypothesis)
6. Debriefing (what do you think the study was about? What were your expectations)

Question 30

what is experimenter bias?

Answer 30

• Sometimes experimenters treat participants differently depending on condition.
• During data collection
• During data coding
• Rosenthal & Fode (1963)
• Student experimenters.
• Tested Maze-Bright and Maze-Dull rats.
• Rats were randomly assigned to be bright or dull.
• Genetic breeding study on rats, create rats that were smart enough to go through maze (bright or dull rats). It was a deception, all were the same and counterbalanced, but student researchers were told some were smart and others were dull. This influenced how the experimenter interacted with the rats.
• Results:
• Still “bright” do better than “dull”
• Gets taller overtime to show they’re learning/getting better with practice.
• Rats are treated differently, handling, encouragement, setting up maze, where they start (subtle unsconious bias in behaviour in favour of bright rats from experimenter).
• Rosenthal & Jacobson (1968)
• Elementary students given a “new” IQ test at beginning of school year.
• 20% randomly identified as “late bloomers”.
• Given standard IQ test at year end.
• Same study on humans:
• The self-fulfilling prophecy effect.
• They randomly labeled some kids as late bloomers, poor now but will boost soon in IQ, others are controls. The late bloomers did better because they had better interactions, support, encouragement from teachers relative to controls (expectations of students impacts on their performance; +/- effects on students, unconsciously our expectations come though in our behaviour).
• Or generally treat people to make them more likely to give the desired outcome we want/expect.

Solutions:
• Experimenters blind to condition
• Double blind – experimenters and participants are both blind
• Experimenters blind to hypotheses (i.e., undergraduate research assistants who do not know hypothesis till the end of the study).
• Automated data collection (computer doesn’t have expectations! Reduce experimenter interaction and bias).

Question 31

what is subjective coding?

Answer 31

• After data is collected the experimenter can still bias the results.
• Subjective Coding can occur when the DV requires human judgment (room for error, bias or expectations)
• In observational studies or developmental research.
• Solution:
  • Automate scoring (time, speech sample and analysis etc.)
  • scoring rubric (set rubric each scorer uses, standardise it)
  • Inter-rater reliability (multiple independent raters with 80% at least agreement)
  • Coders blind to the condition/hypothesis
  • Different data collectors to coders!

Question 32

In statistical hypothesis testing we assume the null hypothesis is ___ until we have enough evidence to reject it.

Answer 32

true

Question 33

what is a false positive and a false negative?

Answer 33

Type I error = α (Greek letter alpha). 
•	Rejecting the null when it is actually 
        true.
•	False positive
•       Innocent man is imprisioned.

Type II error =β (Greek letter beta).
•	Failure to reject the null when it is 
        actually false.
•	False negative
•       A guilty man is set free.

There is always a chance for error! Therefore, they set predetermined criteria for what constitutes as sufficient evidence to reject the null hypothesis.

The alpha (p-value) are the amount of evidence required to reject the null hypothesis

Question 34

Statistical Hypothesis Testing is testing…

Answer 34

Whether I can conclude that there is sufficient evidence to attribute the found difference to the manipulation of the IV?

Question 35

test statistics are a measure of ___. The ___ from ___ compared to ____.

Answer 35

All test statistics are a measure of the variability that is due to your effect, compared to the variability that occurs in your population generally.

Question 36

Standard Error of the Mean (SEM) is an estimate of…

Answer 36

SEM is used to estimate how close the sample mean is to the true population mean.

 The larger the sample we have the
more confident we are that it will be
closer to the true mean. There will be
variability in the mean obtained with
every sample, how spread are these
means?
 SEM = S / square root of the sample
size to scale it to fit the population
 The smaller the SEM the more confident
we are closer to the true mean.

 As we increase the N, the SD becomes
more accurate (closer to the true mean)
 As we increase the N, SEM becomes
smaller.
 The mean we get in sample will always
vary when we sample, we want it as
close to the true mean as possible
(need 30 per condition or smaller if it’s a
within subjects design).

Question 37

The p-value

Answer 37

o The likelihood (or probability) of
obtaining the observed result (or a result
more extreme) if the null hypothesis is
true. (i.e., less than 5% chance that the
results are due to chance or a false
positive error; if the p-value we obtain is
below 0.05 then we reject the null
hypothesis because it is very unlikely
that we are making a type 1 error).
o The likelihood that two samples are
drawn from the same population.
o By convention in psychology, p < .05 is
significant .05 is our alpha

Question 38

Sampling Error:

Answer 38

 In the population, people’s RTs should be normally distributed (some people will be very fast, some people will be very slow, most people will be in the middle).
 If I randomly select 30 of these people and put them in the angry group, and randomly select 30 and put them in the happy group, there is a probability that the two groups won’t have EXACTLY the same mean RT.
 So, there is a difference between my groups before I’ve even done anything to them. EVEN WITH RANDOM ASSIGNMENT. This variability of the observed difference compared to the true difference is called SAMPLING ERROR (i.e., natural variation of the mean which occurs when sampling from the population).
Sampling Error & the T Test Statistic:
 Sampling error impacts the t-test score significantly! If the null hypothesis is true and there is no difference between the groups mean.
 The T-test score will never be 0 because of sampling error (variability between the sample mean and true mean with each sample).
 t = 0
- No difference between groups (they are drawn from the same population)
- X1 – X2 = 0
 Sampling error is chance; will sometimes be bigger or smaller than but never 0 (even if there is no difference between two group means).

*Main Q is weighing up the variance explained by the IV and noise (random error)

Question 39

How often does the null hypothesis produce differences between groups (t-test score) as big (or bigger) than the one obtained?

The sampling distribution:

Answer 39

 Sampling distribution shows me how
often I will get different values of a test
statistic (e.g., t) by randomly sampling
from a single population if the null
hypothesis is true.
 Shape of the sampling distribution
depends on how big my sample is
(degrees of freedom = N – 2; 1 for each
group)
 Shape of the t distribution depends on
the number of people in your sample.
The more people, the more likely t will
be close to 0.
 Red = small degrees of freedom of 3
(shallow with more spread; variety of t’s
in small sample)
 The red ends of the tail is the tiny part
of the distribution where in this area
(less than 0.05%) will these findings be
due to chance (extreme values +/- that
can only occur 5% of the time; 2.5%
either side).
 The larger the sample size and degrees
of freedom the steeper it gets, and the
less likely extreme values are = closer
to true mean! Random differences
between groups not due to IV is less
likely.

Question 40

Critical Value of T

Answer 40

 If my t is very big, then it is very unlikely
my two means come from the same
population. So I will assume that I have
caused my two groups to have different
response times through my
manipulation (my manipulation is guilty).
 Critical value of t
- Value at which there is less than 5%
probability that the t arose through
sampling error.
- We therefore reject the null hypothesis.
- We conclude that there is a significant
difference between groups.

*if it meets the critical threshold of t (at p-value of 0.05; sampling error) we can reject the null hypothesis because the findings are not likely to be due to random chance and is
attributable to the manipulation of the IV to the difference found.

Question 41

One-tailed or two-tailed?

Answer 41

 H0 = no difference between RT to angry
and happy faces
 H1 = RT to angry faces are different than
RT to happy faces
- Bidirectional hypothesis (difference in
either direction, test both tails, 2.5% of
tail at both ends)
- Two-tailed
 H1 = RT to angry faces faster than RT to
happy faces
- Directional hypothesis (one group better
than the other, test one tail; 5% at one
end)
- One-tailed