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Notes for week 4

by: KhloNotes

Notes for week 4 Psych 2110


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Notes for Chapter Four of PSY 211
Elem Statistics Business
Andre Souza
Class Notes
Psychology, hypothesis
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This 3 page Class Notes was uploaded by KhloNotes on Saturday September 24, 2016. The Class Notes belongs to Psych 2110 at University of Alabama - Tuscaloosa taught by Andre Souza in Fall 2016. Since its upload, it has received 4 views. For similar materials see Elem Statistics Business in Psychology (PSYC) at University of Alabama - Tuscaloosa.


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Date Created: 09/24/16
Key:​ ​Bold/Italic = ​important information, ​Highlighted ​= vital for test      From 211 Elementary Statistical Methods (Psychology) ; Professor Souza    Ideas Stressed/Re­stressed in Chapter Four:  ­ Statistics =​ random variables   ­ Inferential Statistics =​ procedures for drawing conclusion based on the scores collected  in a research study but going beyond them; creates an educated guess for studies where it  is impossible to question the entire population    ­ Memorize the Standard Distribution Variance Formula (Depicted below)      Intro to Hypothesis Test   A sample statistic is the best option for making an educated guess      Summary of the five steps of the​ hypothesis­testing procedure:  1. Restate the question as a research hypothesis and a null hypothesis about the populations.  2. Determine the characteristics of the comparison distribution.  3. Determine the cutoff sample score on the comparison distribution at which the null  hypothesis should be rejected.  4. Determine the​ sample's score on the comparison distribution.  5. Decide whether to reject the null hypothesis.    Ex: How conservative is Alabama?   ­ Five students ask a sample of fifty people with a scale (1­10)  ­ Thus, the head researcher has five guesses to average together = Central Limit Theorem     Central Limit Theorem  ­ Given any population, the means of random samples together will converge to a normal  distribution (mean of sample distribution & population are equal)   ­ Standard Distribution Variance (Standard Error of the Mean)    Formula:     σ      √ n   ­ Standard Error =​ How much the sample mean would vary if we took several samples at  the same size from the same sample  ­ More people = more precision     Real World Application   1. Usually just grab one sample  2. Take the z­score for this sample (distance from the mean)   Key:​ ​Bold/Italic = ​important information, ​Highlighted ​= vital for test     Formula: z =  x − M     √nσ       or z =   x̄ − M   SD   Hypothesis Testing Cont.   ­ Objective = ​check whether a set of data agrees with a certain prediction   ­ Prediction = hypothesis → statement about a population, a guess that a parameter  takes a particular numerical value or falls within a particular interval, systematic  way to test claims or ideas about a population   ­ A significance test uses data to summarize the evidence about a hypothesis (comparing  sample statistics or point estimates of parameters) to the values predicted by the  hypothesis  Ex:​ Are men paid more for the same job?   Answer: ▯women < ▯men = if that #10, then ▯women must be less than 10    * if you see x̄women < x̄ men = sample mean (this is purposely the wrong answer, look for the ▯  symbol which is the correct form of the problem)   Objective: ​To determine the likelihood that a sample statistic would be selected if the          hypothesis regarding the population parameter were true     Four Basic Steps (look in the book for more to review)  ­ State the hypothesis  ­ Set a criteria for decision (basically choose a α­level) ​*Has to be done before  observations   ­ Compute the test statistic (collect data)  ­ Compare and make a decision   Ex:    1. Fabio is not gay.   2. How many gay bars he goes to → criteria = 3 bars   3. Stalk him to collect data  4. He goes to 4 → my hypothesis is wrong   Explanation:   Every hypothesis test has two hypothesis   ●  Null ­ H​  →0​tatement about value of a population parameter  ● Alternative ­ H​  or H​ → statement that the population parameter falls into an  1​ a ​  ​ alternative range of values than those stated in null  *Null usually states no effect       Key:​ ​Bold/Italic = ​important information, ​Highlighted ​= vital for test     Good v Bad   ● A good hypothesis is one that can be rejected or falsified   *the absence of evidence is not evidence of absence   **null = crucial role → we can not prove something to be true, but we can prove  it to be false → fail to reject it/rejection   Ex: ​You get a call and a girl says pregnant!   Null Hypothesis: ​Karen is n ​ ot​ pregnant   How to Test: ​Pregnancy Test   Two Outcomes: K ​ aren is, Karen isn’t     Pregnancy Test   Reality                 Not Preg                                             Preg   +  False +  Correct   ­  Correct   False ­    2 possible errors of null hypothesis:   ● Type 1 = ​We can reject the null (​H​ ) when t0 ​null (​H​ ) is true (0 ​se  positive)   ● Type 2 =​ Failure to reject the  null (​H​ ) when0 ​e null (​H​ ) is false0 ​alse  negative) *preventable with more research    One ­ Tailed v. Two Tailed Test   ­ Alternate ways of computing the statistical significance of a test statistic     One ­ Tailed:   Only interested in one side of test, i.e.: Are girls are  smarter than boys? You pay attention to the gender  falling in the five percent zone.          Two ­ Tailed:   Interested in both sides of the spectrum, i.e.: Does  drinking lower your IQ? You pay attention to both  sides.       


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