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Elementary Statistics Chapter 1 - Day 1 Notes

by: Stephanie

Elementary Statistics Chapter 1 - Day 1 Notes Psy 202

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About this Document

These notes cover the first lecture on Chapter 1 for Elementary Statistics.
Elementary Statistics
Matthew Mervin
Class Notes
Statistics, psych202




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This 3 page Class Notes was uploaded by Stephanie on Saturday August 27, 2016. The Class Notes belongs to Psy 202 at University of Mississippi taught by Matthew Mervin in Fall 2016. Since its upload, it has received 46 views. For similar materials see Elementary Statistics in Psychology at University of Mississippi.


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Date Created: 08/27/16
PSY 202 Elementary Statistics  Chapter 1:  Introduction and Background – Day 1 I. Chapters 1 & 2 comprised Homework #1 II. Overview a. People are afraid of the math in statistics which is why people do not like to take  it. III. Why take statistics? a. Some of us are required by our major b. Statistics helps to organize and analyze data for research c. We must learn to question patterns when we first see them and statistics helps to  create that habit d. The purpose of statistics is to help figure out if a pattern is legitimate or not e. Humans are excellent at detecting problems i. This is both good and bad 1. Humans can detect patterns when there are none which can skew  results IV. The Nature of Reality a. Humans have a very deterministic view of the world b. We do not have the ability to see the world as it actually is c. We build a model to try to explain reality d. Statistics helps bridge our view and reality e. The universe is probabilistic  f. We must not think in absolutes, only probabilities  V. Science and Research in Social Science a. Psychology is a social science i. We study humans and their behavior ii. Figuring out patterns of human behavior is difficult VI. What is science? a. First, make observations i. Create or modify a theory b. Create a theory i. Use theory to make a prediction c. Make a prediction i. Design an experiment to test prediction d. Conduct an experiment i. Perform the experiment ii. This is where data and statistics come in  e. Repeat the cycle f. At the end of the experiment we can say that we have found evidence, but it is not confirmed  VII. What is research? a. Qualitative:  Measuring something based on its quality, not its quantity b. Quantitative  (This is what we do) i. Structured data ii. Statistical analysis iii. Objective conclusions iv. Surveys, experiments c. Research is the way we test a hypothesis d. Experiment i. Experimental Design 1. These experiments control an independent variable and see how it  affects the dependent variable 2. Random assignment:  You decide who gets to be in which group in an experiment a. Gives a lot of control over variables b. Best for answering if one things follows another   (cause/effect) c. We lose external validity  i. External validity is how the environment affects the  variables. ii. Quasi­Experiments  1. Quasi­experiments do not use random assignment 2. These experiments are not good at determining cause and effect 3. Gain some external validity iii. Observational Studies 1. Polar opposite of experimental design 2. No control over anything  3. All you do is observe  4. There is a lot of external validity 5. Does not allow you to tell cause/effect  e. Types of statistical analyses depend upon what experiment is used VIII. Designing a study a. Pay attention to a lot of factors  b. Things to consider: i. Which population to generalize to ii. How to gather data iii. Who to include in the sample IX. Populations, Samples, and Techniques a. We are not able to test everyone in a population b. Populations i. We want data of the effects on the population not just individuals ii. Instead of testing the whole population we get a sample and test the subset 1. Use data from sample to generalize back to the population c. Sampling Techniques i. Simple random sampling 1. The sample should reflect the population a. Ex:  If 60% of the population are blue, then 60% of the  sample should be blue 2. Not guaranteed  a. Sometimes the sample does not reflect the population ii. Stratified sample 1. There is some control and is not completely random 2. This forces the sample to match characteristics of the population 3. Large sample sizes are best for representing of the population iii. Cluster sampling 1. Use different subgroups in a large sample group iv. Systematic sampling th 1. Samples are created under a system such as “every 5  person” 2. The sample looks different from the population a. It is not very representative of the population v. Deliberate/ Purposive sampling 1. Used in order to target a specific group in a population a. If you have a lot of information from one group, then you  can ignore them to focus on a different group vi. Convenience sampling 1. Use the people that you can easily get involved a. Ex:  Researchers on college campuses will use college  students for their experiments 2. It becomes obvious who is targeted 3. Very far from being representative of the population 4. There are lots of variables among samples 5. This sampling makes it hard to generalize back to population


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