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# PSYC 261 Ch. 7 Notes PSYC 261

CSU Chico

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This 4 page Class Notes was uploaded by Eryn Dixon on Monday October 10, 2016. The Class Notes belongs to PSYC 261 at California State University Chico taught by Adelaide Kreamer in Fall 2016. Since its upload, it has received 3 views. For similar materials see Research Methods in Psychology in Psychology at California State University Chico.

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Date Created: 10/10/16

Using Statistics: Chapter 7 Interpreting Data ● Raw Data ○ Scores from each individual participant ■ Difficult to interpret ● Two Main Methods of Interpreting Raw Data: ○ Descriptive Statistics ■ Ways to summarize raw data that allows for interpretation ○ Inferential Statistics ■ Statistical procedures used to test hypotheses Descriptive Statistics ● Distribution ○ Set of data/scores ● Three Main Methods from summarizing a Distribution ○ Central Tendency ■ Examines the typical score in a distribution ■ Three Main Measures: ● Mean, Median, Mode ○ Variability ■ Indicates how much the scores in the distribution differ from one another ■ Three Main Measures: ● Range, Standard Deviation, Variance ○ Graphs or Tables ■ Tools for summarizing data in a visual way Central Tendency ● Mean ○ Average ■ All scores added together divided by the total number of scores ● Most common, used for inferential statistics ● Influenced by extreme scores (outliers) ● Median ○ Middle Score ■ Half of the scores are above, half o the scores are below ● Can be provide a more accurately typical score when dealing with outliers ● Mode ○ Most frequent score ■ Used with nominal scales Variability ● Range ○ Difference between the highest and lowest score ■ Very crude measure of variability ● Standard Deviation ○ Average difference between the scores and the mean of the distribution ■ More precise ● Used for statistical tests ● Variance ○ Standard Deviation squared ■ Used for statistical tests Tables and Graphs ● Frequency Distribution ○ Shows the frequency of each response in the distribution ● Bar Graph/Line Graph ○ Shows means for different studies ■ Bar Graph ● Bar height represents size of the mean ■ Line Graph ● Mean graphed as a point, points connected to show difference between mean scores ● Scatterplot ○ Shows the relationship between two dependent variables ■ Includes data points from each participant ● Tables ○ Organized to include the mean for different conditions or groups in a study Inferential Statistics ● Use inferential statistics to determine what can be learned about the population from the sample ● Hypotheses ○ Every research hypothesis contains two hypotheses ■ Scientific?Alternative Hypothesis ● The independent variable has an effect on the dependent variable ● A relationship between variables exists ■ Null Hypothesis ● Opposite hypothesis ○ The independent variable has no effect ○ There is no relationship between variables ● One vs Two Tailed Hypotheses ○ One Tailed Hypotheses ■ Directional ● Only one direction of an effect is predicted ● Ex: Individuals in a hot room will have HIGHER exam performance that individuals in a normal temp. Room ○ In order to be supported, hot room scores have to be higher ○ Two Tailed Hypotheses ■ Both directions considered ● ● Inferential statistics determine whether we have to accept or reject the null hypothesis ○ If null is accepted our hypothesis is not supported ■ We were wrong ○ F the null is rejected our hypothesis is supported ■ We were right ● Inferential statistics assess the probability that we are wrong and the null is true ○ If the chance is high then there is no evidence to reject the null ■ There is likely no effect or relationship ○ If the chance is low then we interpret that as being evidence against the null hypothesis ■ We reject the null and accept the alternative hypothesis that there is an effect or relationship ● Confidence Intervals ○ Use the sample mean to infer the population mean (true score) ■ Confidence interval provides a range of values in which the population mean is found with a particular level of certainty ● ■ Ex: If the sample mean is a value of 82 and we want to be 95% sure we have captured the population mean, we might have a margin error of +/- 3. ● ● Significance Testing ○ How likely is it that the sample mean from the study is just a product of the distribution of the population rather than a result of an effect or a relationship ○ Alpha Level ■ Highest percent chance we are willing to accept that the sample mean is just a product of the distribution of the population ● ○ If the probability (p hypothesis ■ There is a high probability that the sample mean was a result of an effect or relationship ● ○ If the p value is higher than alpha we cannot reject the null hypothesis ■ The sample mean was likely just a result of the distribution of the population ○ Typically use an alpha of %5 ■ Only a %5 chance that the sample mean is a product of the distribution of the population ■ 95% chance that the sample mean is a result of an effect or relationship Significance Test Outcomes ● Errors ○ Type 1 ■ Reject the null when it is actually true ● Say there is an effect/relationship when there isn’t ○ Type 2 ■ Accept the null when it should have been rejected ● Say there isn’t an effect/relationship when there actualy is

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