2024-09-30T17:32:50+03:00[Europe/Moscow] en true <p>0.1 -The scientific attitude, critical thinking, and developing arguments</p><p></p><p>Scientifically Derived ASK</p>, <p>Scientific inquiry (scientifically asking questions) ASK</p>, <p>3 Key elements of a scientific attitude</p><p> +</p><p>How do they support scientific inquiry? ASK</p>, <p>Critical thinking</p>, <p>0.2 - The need for psychological science</p><p></p><p>How do the 3 cognitive biases based on <span class="tt-bg-yellow">common sense</span> become road-blocks to critical thinking?Why are science based answers more valid than common sense?</p>, <p>1) Hindsight bias</p>, <p>Overconfidence</p>, <p>Receiving order in random events</p>, <p>Module 0.3</p>, <p>Scientific Method</p>, <p>Peer reviewers</p>, <p>Theory</p>, <p>Hypothesis</p>, <p>Falsifiability ASK</p>, <p>Theory is useful if it 3 steps</p>, <p>Operational definitions</p>, <p>Replicate</p>, <p>Experimental vs non experimental methlodgy ASK</p>, <p>Non-experimental</p>, <p>Experimental ASK</p>, <p>Case study</p>, <p>Naturalistic obsevations</p>, <p>Survey</p>, <p>Wording effects- check with teacher</p>, <p>Social desirability bias</p>, <p>Self report bias</p>, <p>Sampling bias</p>, <p>Convienience sampling </p>, <p>Representative sample</p>, <p>Random sample- ASK</p>, <p>Population</p>, <p>0.4 Correlation and expirmentation </p>, <p>Correlational</p>, <p>Correlate</p>, <p>Correlation coefficient- check</p>, <p>Variables</p>, <p>scatterplots</p>, <p>positive correlation</p>, <p>Negative correlation</p>, <p>No relationship correlation</p>, <p>Directionality problem</p>, <p>Third variable problem</p>, <p>Illusory correlation</p>, <p>Regression towards the mean ASK</p>, <p>Expirementation and questions - pt 2</p>, <p>Experiment</p>, <p>experimental group</p>, <p>Control group</p>, <p>Randomly assign ASK</p>, <p>Placebo ASK</p>, <p>Single-blind procedure</p>, <p>Double-blind procedure</p>, <p>Placebo effect</p>, <p>Independent variable</p>, <p>Confounding variables ASK</p>, <p>experimenter bias</p>, <p>Dependent variable</p>, <p>Validity ASK</p>, <p>module 0.5 ASK</p>, <p>Research design- what is it used for? What do they depend on?</p>, <p>Qualitative research ASK EX</p>, <p>Qualitative research ASK EX</p>, <p>Structured interviews? ASK</p>, <p>Confederate- how prevent social desirability bias?</p>, <p>Ethical codes</p>, <p>Insitutional review boards (IRBs)</p>, <p>how do psychologists values influence what they study and how they apply their results?</p>, <p>module 6 ASk</p>, <p>Descriptive statistics ASK</p>, <p>histogram</p>, <p>measure of central tendency </p>, <p>Mode</p>, <p>mean</p>, <p>median</p>, <p>percentile rank</p>, <p>skewed distrbution</p>, <p>measures of variation</p>, <p>Measure of variation 1) Range</p>, <p>Measure of variation 2) standard deviation</p>, <p>Normal curve </p>, <p>inferential statistics ASK</p>, <p>when are sample differences between groups reliable-can be generalized differences? 3 factors to keep in mind</p>, <p>Meta analysis</p>, <p>example showing: why is an oberserved difference significant</p><p>ex men and women agression test, gender diff? step1 ASK</p>, <p>null hypothesis</p>, <p>example showing: why is an oberserved difference significant</p><p>ex men and women agression test, gender diff? step 2</p>, <p>Statisical significance and determined by?? ASK</p>, <p>P value</p>, <p>Alternative hypothesis- hypothesis created after exp? prove wrong?</p>, <p>Effect size??? ASK how gotten and more</p> flashcards

AP Psychology

Unit 0

  • 0.1 -The scientific attitude, critical thinking, and developing arguments

    Scientifically Derived ASK

    When data or evidence are scientifically derived, that means they come directly from rigorous scientific experimentation and research.

    -You can use scientifically derived evidence to support or challenge a scientific theory or hypothesis. (Considered reliable- if something is not scientifically derived, its a belief)

    James Randi (1928-2020) was a magician and a skeptic who tested and debunked supposed psychic phenomena by testing if evidence supporting it could be scientifically derived.- If not, unreliable. He sifted reality from fiction.

    Ex: Glowing aura expirement

  • Scientific inquiry (scientifically asking questions) ASK

    Asking questions and conducting observations and investigations to obtain evidence that can create logical explanations and answer those questions (Detective) In many different methods-scientists use them to explore and understand the natural world

    This can include:

    -Thinking about what we already know

    -Thinking critically

    -Reasoning

  • 3 Key elements of a scientific attitude

    +

    How do they support scientific inquiry? ASK

    Scientific attitude: the mindset and approach that individuals have towards science. It involves having a rational outlook, accepting information based on scientific evidence no matter personal opinion, promoting logical thinking, and encouraging systematic doubts and critical thinking- approach with three steps:

    Curiosity: "Does it work?"- when put to the test, can predictions be confirmed?

    Skepticism: (Not cynical) "What do you mean?" "How do you know" Sifting reality from fantasy

    Humility: "That was unexpected! Let's explore further." (Follow new ideas- predictions are not always right- not about opinion)

    The scientific attitude equips us to be curious, skeptical, and humble in scrutinizing competing ideas or our own observations. Curiosity triggers new ideas, skepticism encourages attention to the facts, and humility helps us discard predictions that can't be verified by research.

  • Critical thinking

    Thinking that does not automatically accept arguments and conclusions and does not follow a "gut feeling" examines assumptions, appraises the source, finds biases, looks at evidence, and assesses conclusions.

    Ex: "How do you know that? Do they have an agenda? Anecdote? Evidence justify cause-effect conclusion?

  • 0.2 - The need for psychological science

    How do the 3 cognitive biases based on common sense become road-blocks to critical thinking?Why are science based answers more valid than common sense?

    How do cognitive biases-hindsight, overconfidence, perceive order in random events ills. Road block to critical thinking. Science-based ans- more valid then common sense

    Ex: going outside and cold weather makes you sick

    The scientific attitude equips us to be curious, skeptical, and humble in scrutinizing competing ideas or our own observations. Curiosity triggers new ideas, skepticism encourages attention to the facts, and humility helps us discard predictions that can't be verified by research.

    All make overstmations of importance of common sense- scientific injury can help us overcome such biases and shortcomings

  • 1) Hindsight bias

    the tendency to believe, after learning an outcome, that one would have foreseen it (I knew it all along phenomenon)

    Ex: "Seperration weakens romantic attraction" ask why true- after hearing explanation, say unsurprising- becomes common sense

    (Predicts what has already happened)

  • Overconfidence

    the quality of thinking one knows more than they actually do, especially after seeing the answer- The idea that the accuracy of one's evidence or beliefs is more than it is. (comfort to think we know)

    Ex: Anagrams: water-wreat vs getting just wreat- takes longer

  • Receiving order in random events

    Because we have an eagerness to make sense of the world to make sense of the world (uncomfy to not know) try to find patterns in everything- random sequences

    Ex: flipping a coin- make certain patterns in mind to give the impression of knowing

  • Module 0.3

    The Scientific Method

  • Scientific Method

    The scientific method is a self-correcting process of evaluating ideas with observation and analysis- putting them to the test.

    Steps:

    1- collect data/observations

    2-create a theory

    3-create hypothesis

    4-revise or reject hypothesis via falsifiability, replication

    5-peer review, publish or not

  • Peer reviewers

    Peer review is integral to the scientific method process as it ensures the validity and quality of research. Through rigorous evaluation by experts in the field, peer review helps validate findings, identify errors or biases, and assess the methodology and interpretation of results- after studying this scientific journal or theory and receiving review, the journal editor decides to publicize or not.

  • Theory

    an offered idea that uses an integrated set of universal, logical principles with organized and isolated observations to explain and therefore predict behavior or events- summarizes and simplifies data.

    Ex: "sleep affects memory"

    theory- sleep-related observations organized into a lot of principles (good nights sleep = answer questions well)

  • Hypothesis

    a testable prediction that puts a theory to the test- it is often implied by the theory if the theory is good. When testing a hypothesis it specifies which results support theory and which disapprove it.

    Ex: "When sleep-deprived, people will remember less from the day before."

  • Falsifiability ASK

    Falsifiability is the capacity for some proposition, statement, theory or hypothesis to be proven wrong. You want it to be proven false because if it isn't- if there is no experimental test to disprove the hypothesis, then it lies outside the realm of science. When something is falsifiable, it can further scientific process- its measure of falsifiability has to at least be low.

    Ex: asses how well people remember class stuff they studied either before a good/bad nights sleep- will support or falsify theory.

  • Theory is useful if it 3 steps

    1. It organizes observations (theory), 2. implies predictions that anyone can use to check theory (hypothesis), 3. and stimulates further research or revised theory (falsfiability).

  • Operational definitions

    a carefully worded statement that describes what qualifies as a certain behavior (and therefore when that behavior is being tested for in a study, the exact procedures/operations used in the research study), written objectively and free from vague or interpretative phrases so that anyone can identify if it is occurring or not or if it is replicable in a consistent manner.

    Both variables are given precise operational definitions,

    Independent: which specify the procedures that manipulate the independent variable (the review versus self-testing study method in this experiment) and measure the dependent variable (final exam performance).

    Ex: sleep deprived "at least 2 hours of less than a person's natural sleep."

  • Replicate

    Replication is when, thanks to the clear operational definitions given, one is repeating the essence of a research study, usually with different participants in a different situation to see whether the basic finding can be reproduced.

    -If the results support the finding and stimulates similar results, the reliability of it grows.

    Ex: 1st study about sleep deprivation- aroused curiosity- after many successful replications, we can feel sure of it's findings.

  • Experimental vs non experimental methlodgy ASK

    Methods that we use to test hypothesis and refine theories- falsifiability

  • Non-experimental

    SNACC

    Non-experimental methods such as case studies, surveys, naturalistic observations or correlations are aimed to be purely descriptive and observational, (the basis of science) or to describe the relationship between variables. They do not provide researchers with a cause and effect, and variables are not manipulated.

  • Experimental ASK

    Experimental methods manipulate variables (dependent and independent) to see the cause and effect relationship between them. It aims to provide a detailed description of operations done that allows other researchers to replicate the study and evaluate its validity.

  • Case study

    A non-expiremental technique in which one individual or group is studied in depth in the hopes of revealing universal principles.

    Advantages: effecient, not super expensive, universal truth, interact with people (ask about background)

    Disadvantages: single case can mislead- time consuming, researcher bias.

    Ex:Brain damage. Much of our early knowledge about the brain came from case studies of individuals who suffered a particular impairment after damage to a certain brain region.

    Ex:Children’s minds. Developmental psychologist Jean Piaget taught us about children’s thinking after carefully observing and questioning only a few children.

  • Naturalistic obsevations

    a non-expiremental technique of observing and recording behavior in naturally occuring situations w/out trying to manip/control the situation (now "big science" thanks to social media)

    THEN Ex: chimpanzee observation- parent-child reactions in different cultures.

    NOW Ex: GPS during COVID, watches (fitness,apple, social media)

    Advantages: better understanding of different reactions, dont know being watched, truthful, large number of partcipants, no social desirability bias

    Disadvantages: cannot follow up or interact- inconsistent results and cant ask abt, no isolation of variables (dont know why)

  • Survey

    non experimental technique for obtaining the self-reported attitudes/behaviors of a particular group, usually by questioning a representative, random sample of the gorup- ask people to report their own behavior/opinions - people may shadfe answers in a socially desirable light//direction (depend on how Q worded, respondents chosen)

    Advantage: efficient, cheap, anonymous, large number of people

    Disadvantage: wording effects, anonymous- no follow up, lie, double barreled question

    Ex: 1 in 2 people across 24 countries reported believing in aqliens. Ex: Undereporting cigarette usage, ovveraporting voting

  • Wording effects- check with teacher

    SURVEYS---

    Wording effect describes the possible effects on participants caused by the order of presented words or even the choice of the words themselves. For example, in an experiment a participant is asked to choose a word from a list.-Even small changes in the order or wording of Q can make a big difference

    Ex: revune enhancers vs taxes

  • Social desirability bias

    bias for people's responding in ways they presume a researcher expects or wishes- researchers attempt to phrase questions so they reduce this - sound good

  • Self report bias

    Bias when people report their behavior innacurately- to reduce researches mat pair surveys with other means of measuring behaviors.

  • Sampling bias

    a flaeds sampling process that produces an unrepresentative sample-. A biased sample is the result of collecting a sample from a population that is not random but tends to produce a particular outcome.- also when one generalizes from a few vivid but unrepresntitve cases

    Ex: car model evaluation- two angry- many happy- worth the same-vivid

    Ex:. Asking a group of 9th graders what they believe the speed limit should be on highway-particular outcome

  • Convienience sampling

    collecting research from a group that is readily available, research from

    Ex: your friends, at school, rather than a sample test that represent all students in school

  • Representative sample

    a sample (person, group) from a larger group that accurately and systematically is chosen in a way that represents the characteristic of a larger population-A representative sample is a group or set chosen from a larger statistical population according to specified characteristics.

    larger the better as long as still representative

  • Random sample- ASK

    a sample that fairly represents a population because each member has an equal change if inclusion

    -A random sample is a group or set chosen in a random manner from a larger population.

    Ex: Number people, number generator

    -whys sending all survey questions not work? Consciencious? Best method for generalizing in survey findings?

    How create represenative survey sample? same?

  • Population

    All those in a group being studied, from which samples may be drawn (except for national studies, this does not refer to a whole country's population)

  • 0.4 Correlation and expirmentation

    Correlational research (a non-experimental method) describes the relationship between two or more variables. Experiments attempt to establish a cause-and-effect connection.

    complement each other

    What does two things correlate mean, positive, negative correlate?

  • Correlational

    (non exp method) describes the relationships between two or more variables

  • Correlate

    a measure of the extent to which two factors vary together, and thus of how well either factor predicts the other

    Ex: aptitude test scores- school sucess- how well scores predict school sucess-)

  • Correlation coefficient- check

    a statistical measure/index of the relationship between 2 things (from -1 to +1) - how closely vary together, how well one predicts the other

  • Variables

    anything that can vary and is feasible or ethical to measure

  • scatterplots

    a graphed cluster of dots, each of which represents the values of 2 variables. The slope of the point suggests the direction of the relationship between said variables. The amount of scatter indicates the strength of the correlation (less = stronger)

  • positive correlation

    (perfect positive rare) 2 sets of scores, tend to rise/fall together

    Ex: height and weight

  • Negative correlation

    (perfect negative rare) 2 sets of scores relate inversely, one set up as other down

    Ex: peoples height and distance from head to ceiling

  • No relationship correlation

    no pattern can be detected between variables (too scattered)

  • Directionality problem

    correlational research cannot tell us which variable = cause, which = effect

    Ex: teen social media, depression

  • Third variable problem

    a type of confounding in which a 3rd variable leads to a mistaken casual relationship between 2 others

    Ex: cities with great number churches have a higher crime rate

  • Illusory correlation

    perceiving a relationship where none exists, or perceiving it stronger than it is. When we believe there is a relationship between two things, we are likely to notice and recall instances that confirm our belief. If we believe that dreams forecast actual events, we may notice and recall confirming instances more than disconfirming instances."I can personally influence chance events??"

    comforting

    Ex: gamble, dice- low number hard gentle for high number

  • Regression towards the mean ASK

    the tendency for extreme or unusual scores or events to fall back (regress toward average) - Extrodinary things followed by ordinary. (cause superstitious thinking)- punished for praise, rewarded for criticizing, not nessecarily true

    Ex: bad game- coach yell at- team did good after? helped?

    Ex: outlier grades go to student's average

    Regression towards the mean and illusory correlation relate in that the latter can be a result of misunderstanding or misinterpreting the former, leading to incorrect assumptions about a perceived negative relationship.

    How resemble illusionary correlation?

  • Expirementation and questions - pt 2

    what are the charecteristics of experimentation that make it possible to isolate cause and effect. - Used to show cause of correlations (what started the other)

    Ex: correlation - 2010 smart phone use up, depression of girls up. -Not enough to prove cause and effect

    Expirement- 7 of 9 longitutdinal (over-time) studies, teens social media use=depression

  • Experiment

    a research method in which an investigator manipulates one or more factors (ind. variables) to observe the effect on some behavior or mental process (dependent variable) By random assignment of participants, aims to control other relevant factors.

    advantages: get cause and effect, manipulate variables in controlled enviroment, can ask questions after debrief

    disadvantages: expensive time consuming=flawed, social desiraility up, not natural, difficult to connect to real world, unethical sometimes

    Isolate the effects of one or more factors by: 1)manipulating the factors of interest 2) holding constant ("contrasting") other factors

  • experimental group

    EXP- osed

    In an experiment, the group exposed to the treatment, that is, to one version of the ind. variable

  • Control group

    CON-contrasts with experimental CON-experimental

    In an experiment, the group not exposed to the treatment; contrasts with the experimental group and serves as a comparison for evaluating the effect of the treatment

  • Randomly assign ASK

    CHANCE:

    C-control

    N-No differences

    E-expiremental

    SS- sampling =survey

    Assigning participants to experimental and control groups by change, thus miminizing differences (presisting) between the different groups- equalizes the two groups. - if people different at end, has effect.

    ? In experiments, random assignment ensures that confounding variables have an equal chance of appearing in the experimental and control conditions. Therefore, random assignment controls for possible confounding variables.?

    (Diff between random samples- survey representtive ex vs random assignment= equalizes exp and control groupsEx: random number on a table, flip of a coin.

  • Placebo ASK

    In place of a bonafide operation

    An inactive substance or other intervention that looks the same as, and is given the same way as an active drug or treatment being tested: more there for psychological benefit of the patient. Appears real, no benefit on disorder/illness- does not have active ingredient that makes work

    Ex: sugar pills, inert injections, sham surgery

    why used?

  • Single-blind procedure

    Single Blind ProcedureS, B-Social desirability bias P- participants (1)

    an expiremental procedure in which the research participants are ignorant (blind) about whether they have received the treatment or the placebo- decreases social desirabilities affect.

  • Double-blind procedure

    Blind Participants

    B-both

    Par-participants, re-research

    -commonly used in drug evaluation studies.

    when both research participants and the research staff are ignorant about which research particpiants have received the treatment or placebo- decreases experminter bias and placebo effect-Because patients don't know what they're getting, their belief about what will happen doesn't taint the results.

  • Placebo effect

    Place- PeaceE-expectations

    experimental results caused by expectations alone; only effect on behavior caused by the administration of an inert substance or condition, which the recipent assumes is an active agent- thinking get a treatment to reduce anxiety, happy, reduce pain., apprehension

    Ex: atheletes have ran faster when given a supposed performance-enchancing drug (more expensive-more real seems)- to know how effective therapy really is, researches must control for a possible placebo effect.

  • Independent variable

    Vary independently of other factors

    In an experiment, the factor that is being manipulated; the variable whose effects are being studied

  • Confounding variables ASK

    in an exp, a factor other than the factor being studied that might influence a study's results.

    same as third variable problem?

    ??Using random assignment, they can minimize confounding variables, such as preexisting differences between the experimental group (exposed to the treatment) and the control group (given a placebo or different version of the treatment).??

  • experimenter bias

    E- effect R-results

    B-beliefs

    bias caused when researchers way unintentionally influence results to confirm their own beliefs - reduced by double blind procedures

  • Dependent variable

    in an experiment, the outcome that is measured; the variable that may change when the independent variable is manipulated- dependent of others

  • Validity ASK

    V-variables

    D- determine

    The extent to which a test or experiment measures or predicts what it is supposed to ??(see also predict validity)??

  • module 0.5 ASK

    research design and ethics in psychology

    NEED KNOW??

    which research design to use??

    need know twin study, longitudinal, or cross-sectional??

    animals ethics?

    how do psychologists values influence what they study and how they apply their results??

  • Research design- what is it used for? What do they depend on?

    Research design in psychology is the specific approach used to scientifically collect, analyze and then interpret data. In psychology and the behavioral sciences, the data is typically observations of the behavior of people, but animal behavior is also widely studied.Its design choices are determined by a researchers intepretations of

    -lab findings- generalize diff pop?

    -time periods

    -cultures

    -diversity

    -expenses

    -confounding variables

    -ethical- is it ethical?

    This is so they provide meaningful results.

  • Qualitative research ASK EX

    Answers "how much/many" research method that relies on quantifiable, numerical data

    Ex: correlational research, surveys to collect numerical data, Likert Scale= questionnnaire responses fall on continum- from strongly agree to strongly disagree.

  • Qualitative research ASK EX

    Answers "hows" and "whys" based on people's experiences, perceptions, and behavior. search method that relies on in-depth, narrative data that is not translated into numbers

    Ex: Observation (natural), expirements (some), case studies, structured interviews

  • Structured interviews? ASK

    It involves scheduling of questions where the researcher will ask each respondent the same questions in a similar way. Also known as standardized interview, in structured interview the set of questions are predetermined, which are similar in wording and order [1].

  • Confederate- how prevent social desirability bias?

    Con- federal-lie, in charge

    ETHICAL?

    Individuals who seem to be participants in a research expirement but are actually a part of the research team

    Ex: Solomon Asch line expirement

  • Ethical codes

    1) informed consent/assent -giving potential ADULT/MINOR participants enough information about a study to enable them to choose whether they wish to participate or not.

    Ex: trigger warnings, etc

    2) debriefing-the post experimental explantation of a study, including its purpose and any deceptions, to its particpants.

    3) protection from any unordinary harm

    4)confidentiality in individual information about particpants and results

  • Insitutional review boards (IRBs)

    boards established by universities and research organizations to enforce ethical stnadards (at least 5 people, 1 scienttist, 1 non sci, 1 community rep) screen research proposals, safegaurd "the rights, welfare of humans"

  • how do psychologists values influence what they study and how they apply their results?

    -choice of topics,

    -facts- what want/expect to see

    -labels for behavior

    -advice

  • module 6 ASk

    statisical reasoning in everyday life

    need know practical significance?

    confidence interval?

  • Descriptive statistics ASK

    Compare averages Descriptive statistics refers to a set of methods used to summarize and describe the main features of a dataset, such as its central tendency, variability, and distribution-overview of the data and help identify patterns and relationships- describe charechteristics of groups- descriptive statistics state facts and proven outcomes from a population

    uses both measures of varation and central tendency

  • histogram

    a bar graph depicting frequencies in distribution (differences in graph size based on how big y axis is)- can be manipulated to give effect of bigger difference or lesser difference.

  • measure of central tendency

    Central tendency is defined as “the statistical measure that identifies a single value as representative of an entire distribution- usually the middle value of its distribution”[2] It aims to provide an accurate description of the entire data. It is the single value that is most typical/representative of the collected data. there are 3 different central tendency measurements:

  • Mode

    the most frequently occuring scores in a distribution (biamodial dist occurs when there are 2 frequently occuring scores)

  • mean

    the arithemetic average of a distribution, obtained by adding the scores and then dividing by the number of scores- can be influenced by skewed distribution, check

  • median

    the middle score in a distribution; half the scores are above it and half below, not influenced by skewed distribution

  • percentile rank

    the percentage of scores that are lower than a given score

    Ex: 79th precentile in math comp, score higher than 79% of your peers

  • skewed distrbution

    a representation of scores that lack symmetry around their average value (lospided)- shown on histogram-- Data that is positively skewed has a long tail that extends to the right. Data that is negatively skewed have a long tail that extends to the left.

    ex: income data- mean median and mode tell different stories- mean biased by extreme (few) incomes(rich)- median wealth unchanged- skewed representation.

  • measures of variation

    decribes how scattared or sprewad out the values in a data set are using two measures: range and devation.

    - averages from scores with low variability are more consistent, more reliable

    Ex: basketball player usually scores 13-17 points, better than 5-25 points variaqbility

  • Measure of variation 1) Range

    the difference between the highest and lowest scores in a distribution (crude estimate of variation)

  • Measure of variation 2) standard deviation

    a computed measure of how much scores vary around the mean score- gauges w/scores packed together or dispersed- info from each score, how each differ from the mean

    ex: class a and b have the same mean, and one standard deviation away from that mean (15 or 5 maybe) can be both 75% of people. Very different data variations

    Ex: highests, intelligence, symetrical, bell shaped distriution- also known as normal curve. Most % near mean, farther- more extreme sides less %.

  • Normal curve

    bell shaped even curve- distrubtion of many types of data- most near mena and fewer near both extremes- divided by standard deviations- more standard deviations away from mean, less %. rouhgly 68% inside first deviation, 95% in second

  • inferential statistics ASK

    Inferential statistics is numerical data sample (average) that allows one to infer the probablitiy of a hypothesis or charechteristic being true of a population instead of just by chance or sampling errors,

    or a sample (average) difference to a generalized difference of two things(eg, expiremental and control group). To appropriately estimate a population characteristic, or parameter, a random and unbiased sample must be drawn from the population of interest. Then we can find how statisically significant/reliable the differances are.

    (is the data a result of a real difference or a sampling error/bias)?

  • when are sample differences between groups reliable-can be generalized differences? 3 factors to keep in mind

    are representative of the larger population being studied, demonstrate low variability, on average; and consist of many cases.

    1- importance.represented sample is better than biased samples

    2- bigger better than smaller samples- precise, repplicable

    3- more estamtes better than fewer- conduct many studies, combinhe all estemates.

  • Meta analysis

    a statstical prodecure for anaylzing the results of multiple studeis to reach and overall conclusion- based on past research

    ex:The subjects from all eight studies (total: 860 subjects) were pooled and statistically analyzed to determine the effect of the relationship between wearing sunscreen and melanoma.

  • example showing: why is an oberserved difference significant

    ex men and women agression test, gender diff? step1 ASK

    1. begin with assumption no real difference exists betwen groups- NULL HYPOTHESIS-

    2

  • null hypothesis

    In scientific research, the null hypothesis is the claim that the effect being studied does not exist. - data just due to sampling error....

    The null hypothesis can also be described as the hypothesis in which no relationship exists between two sets of data or variables being analyzed- variables cant influence each other- want to disprove.

  • example showing: why is an oberserved difference significant

    ex men and women agression test, gender diff? step 2

    2. Then, using statistics, they evaluate whether the observed gender difference is so rare that it’s unlikely to fit the null hypothesis. (HOW WORk??)) If so, they reject the null hypothesis of no differences, and they say that the result is statistically significant.

  • Statisical significance and determined by?? ASK

    a statistical statement of how likely it is that a result (such as a difference between samples) occured by chance, assuming there is no difference between the populations being studied.(null hypothesis statement)- the higher the degree of statisical significance, the more likely the null hypothesis is wrong and there is a difference.Determined by:

    1. when averages from 2 samples are precise estimates of their respective populations (many obs that have low variability)- more sig dif.

    ex: less variaibility in womans and mens agression scores- more times this is observed, more likely correct

    2. additionally difference between variations are large- both tie together, cant have one without other

    3. P-value

    Ex: study helped students, exists

  • P value

    values provided by psychological tests- indicate the likeliness of your data occurring under the null hypothesis (if probability value is less than 5%, that means the null hypothesis has a 5% change of being true for your data- based on chance- if more than 5% null hypothesis considered too plausible and therefore data is not statistically significant- if less than 5%, significant.)

  • Alternative hypothesis- hypothesis created after exp? prove wrong?

    the statement you use when attempting to disprove the null hypothesis. (We want to prove it) -states opposite'

    Ex: there truly is a gender difference when it comes to irritability.

  • Effect size??? ASK how gotten and more

    The strength of relation between 2 variables (when goes up, effect size goes up) Effect size tells you how meaningful the relationship between variables or the difference between groups is. It indicates the practical significance (significance in real world) of a research outcome. A large effect size means that a research finding has practical significance, while a small effect size indicates limited practical applications.

    ex: tested the intelligence of first-born and later-born individuals. statistically significant tendency- first-born individuals to have higher average scores than their later-born siblings

    difference was only about 1.5 IQ points, so the vast majority of IQ is determined by factors other than birth order. There were 20,000 people in the study, so this difference was “significant,” but it had little practical importance.

    EX: how much help, difference made