COMMON SENSE
people believe info simply because it is part of a collective wisdom
but just because someone comes to a conclusion doesnt mean its tru

SUPERSTITIONS and INTUTION
gaining knowledge based on subjective feelings
gaining knowledge without being conspiciously aware of where that knowledge comes from
interpreting random events as been causally related
(observations and patterns)

INFO FROM AUTHORITY
gain knowledge from authority figures
but is the person actually speaking outside of their field of expertise etc.?
is tehre enough evidence to support their claim?
are there any biases

INFO FROM TENACITY
gaining knowledge by hearing info so often that you accept it is true
failure to change your opinion despite evidence that contradicts your belief
hard to update beliefs.
We don't know if the evidence was ever accurate
don't try to evaluate the claim
difficult to challenge it again.

AVAILABILITY HEURISTIC: A rule of thumb for estimating probabilities based on the ease of with which instances or occurences can be brought to mind.
ie. are there more words begining with K or more words with K as the third letter.
easier to come with words begining with K
but actually theres more words with K as the third letter.

• the OBJECTIVE ANALYSIS and EVALUATION of an issure to form a JUDGEMENT
• Rational
• Analytical
• Logical
• Skeptical
• Open Minded
• Able to update your opinion based on the evidence

HARD DETERMINISM: Human behaviour and actions are wholly explained by pre-determined external factors
no free will, everything is fated

SCIENTIFIC DETERMINISM: There is some underlying systematic order to many phenomena in the universe

• All events have a meaningful causal explanation
  
  

(HOWEVER, this can go too far. Not everything that happens must have unifying single explanation)

IF You have MULTIPLE EXPANATIONS, you always CHOOSE THE SIMPLER EXPLANATION

When you have competing hypotheses that are qually good at predicting the results, the hypothesis with the fewest assumptions should be selected.

Determine whether all claims are necessary for the claim

KEEP IT SIMPLE.

ie. i saw bradd pitt in the city yesterday
evidence: picture on the phone

ie. I was visited by ALIENS last night
low probability
needs extraordinary evidence

especially think about this in relation to things like neuroscience

• claims should be testable
• you must be able to devise a way to test your claim or observe an event that is based on this claim
• you must be able to provide evidence to support this claim

Verificationism: You must be able to provide evidence that supports your claim; verify that it is 100% true

FALSIFICATION (Karl Popper)

• build your theory with the assumption that you COULD be incorrect.
• does not mean that you need to prove your claim is wrong
• but it must be possible for you to find evidence that refutes your scientific claim
• your claim should allow for the possibility that you are incorrect
  - good scientific theories must be able to be falsified

TURN OVER E and 7.
E to verify the claim.
7 to falsify it.
YOU DONT turn over 4 because the same logic doesn't work backwards. The CLAIM is that on the cards with the vowel, there will be an even number. Not that the cards with the even number will have a vowel.
And you can't use K to falsify because it's not a vowel

LOOK FOR EVIDENCE THAT VERIFIES EVIDENCE BUT ALSO EVIDENCE THAT COULD FALSIFY IT

INDEPENDENT variables

• cannot be effected by the participants behaviour
• THE THING that is being MANIPULATED

• the thing that you change

  
  

• different levels. different conditions. controls and such

TRUE independent: something randomly assigned or randomly allocated (around the 5:40 mark)

ATTRIBUTE VARIABLE

• individual difference variables that fall on a spectrum
• ie. introversion, is a natural variable but to what degree is an individual person introverted?
• depression is on a spectrum. individual
• adhd on a spectrum
• level of risk taking
• anxiety
  

Operationalising variables allows you to specify exactly what you mean in your hypothesis/theory

• allows for the variable to be measured/quantified
• allows you to indirectly define unobservable variables
• can't just measure "hunger" on a ruler or weighing scales

An Operational definition is a detailed description of the procedures or operations used to measure or manipulate the variables

Involves providing CLEAR INSTRUCTIONS about how you have defined a variable and how it can be measured or quantified

IMPORTANT because not everyone interprets the same variables in the same way, and it ensures that the hypothesis is clear.

states that a relationship should exist between variables, the expected direction of the relationship between the variables and how this might be measured.

Many hypothesis are tested and data collected before a theory is formed.

can also lead to further questions and hypotheses

Conclude

• conclusions are the researcher's interpretation of the evidence
• based on the results of the experiment
• explain the results of the experiment

The scientific method is dynamic

• must be able to UPDATE your hypothesis when there is a LACK of DATA to SUPPORT it
• must be able to DISCARD your hypothesis when the EVIDENCE REFUTES it

Reducing Error: error is reduced with

• many participants - INDIVIDUAL DIFFERENCES ERROR
• many measurements - MEASUREMENT ERROR
• many occasion
  
  

an average of scores is more reliable than ind. scores

the CONSISTENCY/REPEATABILITY of the results of a measurement

are the results of the measure CONSISTENT?

• do i get the same measurement three times in a row etc.

The degree to which observers agree upon an observation or judgement

The degree to which all of the specific items or observations in a multiple item measure behave the same way.

• ie. measuring intelligence, all the items should equally measure intelligence

Measure inter-observer reliability with correlations

• positive relationship between the scores of each observer
  
  

To have HIGH INTER-OBSERVER RELIABILITY we want BOTH observers to agree

• the HIGHER the CORRELATION between observer judgements, the more reliable the results

HIGH INTERNAL RELIABILITY shows the entire measure is CONSISTENTLY measuring what it should be

divide the measure into TWO HALVES, compare like with like and look at the correlation between individuals' scores on the two halves

WE WANT MORE ITEMS TO MEASURE TO REDUCE ERROR.

the reliability of a measure to produce the same results at different points in time or occasions

important to show that the test or measure CONSISTENTLY measures the construct we are interested in, provided no other variables have changed.

A LARGE DIFFERENCE in scoews between an IDENTICAL TEST taken by the SAME PERSON during TEST 1 and TEST 2 will suggest low test-retest reliability

REPLICATION: can we replicate the results when all variables and conditions remain the same.

• must have exidence from multiple experiments
• MORE TIMES a result is REPLICATED, more LIKELY it is that the findings are ACCURATE and not due to error.

refers to how well a measure or construct actually measure or represents what it claims to.

DOES IT TEST THE AIMMM?

focused on whether the research design and evidence allows us to demonstrate a clear cause and effect relationship.

HIGH INTERNAL VALIDITY when the research design establishes a CLEAR and UNAMBIGUOUS explanation for the relationship between two variables.

REQUIREMENTS FOR CAUSALITY:

1. COVARIATION: is there evidence for a relationship between the variables
2. TEMPORAL SEQUENCE: one variable occurs before the other
3. ELIMINATE CONFOUNDS: explain or rule out other possibilities