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Psy 202 Wk 7 Notes

by: Anna Ballard

Psy 202 Wk 7 Notes Psy 202

Anna Ballard
GPA 3.33

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About this Document

These notes cover ch. 11.
Elementary Statistics
Dr. Melinda Redding
Class Notes
Psychology, Statistics
25 ?




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This 3 page Class Notes was uploaded by Anna Ballard on Thursday October 13, 2016. The Class Notes belongs to Psy 202 at University of Mississippi taught by Dr. Melinda Redding in Fall 2016. Since its upload, it has received 12 views. For similar materials see Elementary Statistics in Psychology at University of Mississippi.


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Date Created: 10/13/16
Lecture 1 10/12/16 Ch. 11 –> The Basics of Statistical Inference: Tests of Location 2 Important Questions in Statistics Which analysis should I use? How should I interpret the results? STATISTICAL INFERENCE Inferential Statistics • General classes of statistical analysis - Parametric Statistics – see if sample fits in with parameters of population o Assume normal distribution (continuous scores – interval or ratio) o Measure of central tendency used – mean (grand mean) o Get sample from distribution and mean of that sample (sample mean)  Standard deviation - Non-parametric Statistics – non-normal distribution or ordinal/nominal scores o Does not make any assumptions about the population - Resampling – Builds up sampling distribution using simulations o empirical/relative frequency side o only can use this today because we have computer power that can handle it  before, no human would want to sit out and figure out that large of a data set o does not make any assumptions about the population The implications 1) sampling error o sample mean is a little lower than grand mean 2) sampling bias - most use simple random sampling – this isn’t the biggest problem (don’t worry about it) 3) sample comes from a different population o sample mean way lower than grand mean (2 or more standard deviations away) Predictions, not descriptions • Using data in front of you to make predictions of data not yet obtained AN OVERVIEW OF THE PROCESS OF NULL HYPOTHESIS TESTING Why humans are so naturally bad at statistics.. • Tendency to see patterns when they don’t actually exist • Naturally bad at probability – mind not capable of comprehending infinity • Supposed to assume patterns do not exist but we don’t - look for something that doesn’t exist and find evidence that says you’re correct - instead, we need to look for information that proves we are wrong o null hypothesis  look for evidence that disproves what you believe Presumed innocent… • Bring you into court because they think you did something wrong - jury told to presume the opposite o innocent until proven guilty - when sufficient evidence comes up, then you can say that assuming innocence is probably not true - jury gets a verdict if sufficient evidence – guilty - jury gets a verdict if no sufficient evidence – not guilty aka no sufficient evidence to say they are With null hypotheses… • Null hypothesis – anything that looks like its related is just due to sampling error - relationship does not exist - not guilty • Alternative hypothesis – any evidence that you find on the sample levels shows a relationship on the population levels - guilty A CLOSER LOOK AT THE PROCESS (OF TESTING HYPOTHESES) IQ and Chess Champions • IQ already normally distributed • Start with research question - question about the relationship between variables - one variable will be IQ score and the next will be if chess champion • null hypothesis – no difference between chess champions and normal people - start off assuming this is true • alternative hypothesis – they do differ from everyone else • average IQ (µ) = 100 with standard deviation () = 15 - significant difference: distance on sample level is larger than sampling error alone o p < 0.05 - reject null hypothesis and claim support for alternative The Example in Detail • come up with sampling distribution - mean, grand mean, and standard deviation o grand mean equals mean • Figure out how spread out the sampling distribution is - take raw score for standard deviation… • Take sample mean and compare it to distribution and see if it falls between most extreme scores - regions of rejection marks cutoff for lowest and highest 2.5% - if falls more than that, more than just sampling error effecting it o reject null hypothesis Two-tailed • H : µ = 100 (null hypothesis) 0 • H 1 µ ≠ 100 (alternative hypothesis) One-tailed (positive) • H 0 µ ≤ 100 • H 1 µ > 100 One-tailed (negative) • H 0 µ ≥ 100 • H 1 µ < 100 • One tailed test makes it easier to reject the null hypothesis - because the critical value is closer to grand mean • Because of fact one tail makes it easier, people start off directing a 2 tail hypothesis but then they look back on it they will change it to one tail because it will reject their null - cannot change hypotheses after you’ve done the analysis


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