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- 1. AP PSYCHOLOGY Descriptive and Inferential Statistics Example from “A Lesson on Correlation”, AP Psychology Curriculum Module. Amy Fineburg, 2009. Adaptations created by R. Lochel & M. Kunz, 2011 & 2012
- 2. Case Study- Pellegra • In the early twentieth century, thousands of Americans in the South died from pellagra, a disease marked by dizziness, lethargy, running sores, and vomiting. • Finding that families struck with the disease often had poor plumbing and sewage, many physicians concluded that pellagra was transmitted by poor sanitary conditions.
- 3. DESIGNING A STUDY- Components of experimentation- Plan to execute, execute the plan • Independent variable: Experimenter manipulated • Dependent variable: Behaviors measured/observed •Is the conclusion plausible? –Are there alternate explanations? •Confounding variables identified.
- 4. Types of Studies- Methods of Data Collection Pro – Obtain detailed information about the characteristics and behaviors of subjects. – May discover unusual features. Cons • Cannot generalize to the population. • Might be subject to cultural and societal values (which could change over time) Case study: choose a number of subjects to study a singular behavior in detail. Summarize results. Attempt to find parallels to other cases.
- 5. Types of Studies- Methods Correlational study Organize existing data into explanatory and response variables: 0 5 10 15 20 25 30 35 0 10 20 30 40 50 60 70 80 90 100 %ofpeoplewhocontractPellegra % of homes with poor plumbing / sewage Sanitation vs Pellegra Rates
- 6. Types of Studies • Correlational study: examine the strength of the relationship between variables. Pros: Can reveal relationships. Allows for prediction. Demonstrates patterns of behavior Indicates likelihood that one behavior may happen in the presence of another. Cons: Cannot be used to prove cause and effect. Often misidentified as causation.
- 7. Case Study- Pellegra • In contrast, Public Health Service doctor Joseph Goldberger thought that the illness was caused by an inadequate diet. • He felt that the correlation between sewage conditions and pellagra did not reflect a causal relationship, but that the correlation arose because the economically disadvantaged were likely to have poor diets as well as poor plumbing.
- 8. DESIGNING A STUDY • Goldberger sought to prove that diet, not sewage, was the cause of pellegra. • In order to “prove” a cause-effect relationship, an experiment must be designed and carried out.
- 9. DESIGNING A STUDY- Experimentation • Experimental design • CONTROL Group: Need a comparison group in order to reduce alternate explanations. Base group provides “natural” conditions. • Subjects, Sampling, & RANDOMIZATION: Subjects must be randomly assigned to treatments, in order to “smooth out” confounding variables. Minimizes bias. Randomization helps to generalize results. • Polls and Politics example of sampling not of experimentation- psych in action! Read the article. • REPLICATION: The experiment should be carried out with enough subjects (Minimum 50) to establish a clear pattern. Other skeptics should be able to test your hypothesis (and find the same conclusions) to ensure reliability and validity.
- 10. Sampling and Polls- Politics and Psych Briefing: • Polling as we know it today began in 1936, when a young statistician named George Gallup conducted the first poll using statistical modeling. He accurately predicted that Franklin D. Roosevelt would trounce Alf Landon. For decades after that, the polling bus. • How does polling work? Sampling public opinion, George Gallup once said, is like sampling soup: One spoonful can reflect the taste of the whole pot, if the soup is well- stirred. • In other words, it’s all about finding a sample that reflects the larger population. Polling is based on the laws of probability. • According to probability theory, it’s not necessary to sample the opinions of all 300 million Americans; a much smaller sample can reflect the larger population—if that sample is truly representative. • For national polls, most pollsters use a sample of 1,500 as a rule of thumb
- 11. DESIGNING A STUDY • Experiment: use of a comparison group to establish a relationship • Pros: – Can be used to establish cause-effect relationships. – Stress the comparison of variables • Cons: – May be difficult to simulate “real-world” conditions. – Ethical concerns
- 12. Pellegra Study Design • Goldberger asked two groups from a Mississippi state prison farm to volunteer for an experiment (in exchange for a pardon from their crimes). Pellegra didn’t pre-exist in the prison. • Group 1 (Experimental Group) was given the high- carbohydrate, low-protein diet (independent variable) that Goldberger suspected to be the culprit, while Group 2 (Control Group) received a “normal” balanced prison diet. • Within months, the former group was ravaged by pellegra (measured dependent variable), while the latter showed no signs of the disease.
- 13. Pellegra Conclusions • The pellagra symptoms disappeared when the volunteers were given meat, fresh vegetables, and milk. • Despite this conclusive evidence, Goldberger had trouble convincing others what he had found. • He spent the rest of his life looking for what exactly was missing in the diet that caused pellagra, but this would not be uncovered until after his death. He also was thwarted by the medical world's obsession with infectious disease, newly understood and in some cases treatable, and the political world's resistance to hearing that poor social conditions could cause disease. • In 1937, researcher Conrad Elvehjem found that nicotinic acid, or niacin, prevented and cured pellagra in dogs. It works as well in humans. Niacin is one of the B vitamins. During the 1930s, great strides were made in understanding the way vitamins work in the chemistry of our bodies.
- 14. DESCRIPTIVE STATISTICS • What comparisons can we make between the heights of males and females?
- 15. DESCRIPTIVE STATISTICS • Descriptive Statistics describe sample data • Measures of Center – Mean – Median
- 16. DESCRIPTIVE STATISTICS • Measure of Spread – Standard deviation
- 17. DESCRIPTIVE STATISTICS • A man has a height of 6’4”. How unusual is his height, relative to the population of adult men?
- 18. DESCRIPTIVE STATISTICS • The Normal Distribution – An idealized distribution of a population. – Heights – IQ scores – “Natural” phenomena – Sampling distributions • The 68-95 rule
- 20. INFERENTIAL STATISTICS- Apply to Goldberger Study • What conclusions can be drawn from the Pellagra study? – An effect is present (weak diet causes Pellagra) – The results can be explained by the random assignment of subjects to treatments. • Inferential Statistics allow us to reach conclusions which extend beyond the immediate data alone.
- 21. INFERENTIAL STATISTICS- Hypothesis Matters • In Statistics, a hypothesis test allows us to measure the results of an experiment • Here, the default hypotheses (or the null hypothesis) states that there is no difference between the two treatments in the development or Pellagra. • The alternate hypothesis says that the treatments make a difference in the onset of Pellagra (cause and effect). –Researchers usually seek to prove the alternate hypothesis.
- 22. INFERENTIAL STATISTICS- Probability in Psychology- •P-value – The probability of observing the measured results of an experiment to determine if no effect is present. – A low p-value (usually below 5% or valued at .05) means the results are statistically significant. » A low p-value often allows us to determine that an effect is present, and is applicable to generalize conclusions to a population. » P-Value < .05 = results are due to more than chance/coincidence. Conclude that there is a cause-effect relationship.
- 23. INFERENTIAL STATISTICS- Probability in Psychology-