• 7 Pages

#### lecture6

Wilfrid Laurier, APSY 301

Excerpt: ... Lecture 6 inferential statistics Research hypotheses Statistical hypotheses Acceptable risks `Real world model' Decision rules Experiment report Inferential statistics Research hypotheses general questions; for example: does ESP exist is early school entrance useful are we learning to be more tolerant of other cultures Inferential statistics Statistical hypotheses ESP if a person guesses card colour 80% or more often H:p(s=1) = .8 random variable S guess result, s = 1, correct guess; s=0, incorrect no ESP => H: p(s=1) = .5 Inferential statistics Acceptable risks One of two types of errors might be committed: Type I error: is committed when a true hypothesis is rejected risk level Type II error: is committed when we fail to reject a false hypothesis acceptable risks are established by whoever commissions the study, denoted by and Inferential statistics Real world model: let p(s=1) = x and s=0)=y then for a sample of size n we have that the binomial theorem (x + y)n describes the distr ...

• 1 Pages

#### InfStatsIntro

Bryn Mawr, MATH 104

Excerpt: ... Introduction to Inferential Statistics Introduction to Inferential Statistics So far we have been doing descriptive statistics. We take a set of data and we nd the best way to describe, summarize, and display it. We consider both individual variables and relationships among variables. We use the information we obtain to draw conclusions and make hypotheses concerning the subjects who provided the data. Introduction to Inferential Statistics Inferential statistics is the science of extrapolating the results of a study to make claims about larger population. Introduction to Inferential Statistics An employer needs information about its 252 employees for insurance purposes. The insurance company wants to know the average age, height, weight, and various other characteristics of the employees. Rather than asking all 252 workers to stop what theyre doing to ll out a survey, the employer decides to randomly select 50 employees and extrapolate their characteristics to the rest of the rm. Here th ...

• 2 Pages

#### Fair Game Sheet â�� Midterm 1

UCSB, CHEM CHEM 109C

Excerpt: ... Fair Game Sheet Midterm 1 Introductory Lecture Types of psychology Brain-in-the-vat problem History and Research Methods Major philosophical quandaries: - mind-body dualism / monism - free will vs. determinism - nature vs. nurture Deductive vs. inductive methods Qualities of the scientific method - falsifiability of hypotheses - replicability of findings - parsimonious explanation - devaluation of anecdotal evidence Naturalistic observations (definition) Case histories (definition) Correlational studies Correlation vs. causation Experiment (definition) Experimental and control conditions Dependent, independent variables Purpose of descriptive vs. inferential statistics ...

• 31 Pages

#### lecture1

CSU Northridge, MR 31841

Excerpt: ... cans is selected and the average (mean) amount of time watching television is 4.6 hours per day. (sample statistic) Example 3: Currently, 42% of the governors of the 50 United States are Democrats. (population parameter) Quantitative data consist of numerical measurements or counts. Qualitative data consist of attributes, labels, or nonnumeric entries. Example 4: Quantitative data: the incomes of college graduates Qualitative data: the genders (male/female of college graduates. Branches of Statistics Descriptive statistics is the branch of statistics that involves the organization, summarization and display of data. Inferential statistics is the branch of statistics that involves using a sample to draw conclusions about a population. A basic tool in the study of inferential statistics is probability. Question 1: Does this numerical value describe a population parameter or a sample statistic? In New York City, there are 3250 walk buttons that pedestrians can press at traffic intersections, and 2500 of them d ...

• 2 Pages

#### lecture1

CSU Northridge, MR 31841

Excerpt: ... ans is selected and the average (mean) amount of time watching television is 4.6 hours per day. (sample statistic) Currently, 42% of the governors of the 50 United States are Democrats. (population parameter) Quantitative data consist of numerical measurements or counts. Qualitative data consist of attributes, labels, or nonnumeric entries. Example 3: Quantitative data: the incomes of college graduates Qualitative data: the genders (male/female of college graduates. Branches of Statistics Descriptive statistics is the branch of statistics that involves the organization, summarization and display of data. Inferential statistics is the branch of statistics that involves using a sample to draw conclusions about a population. A basic tool in the study of inferential statistics is probability. Question 1: Does this numerical value describe a population parameter or a sample statistic? In New York City, there are 3250 walk buttons that pedestrians can press at traffic intersections, and 2500 of them do not work. ...

• 9 Pages

#### 194175

Tufts, OCW 194175

Excerpt: ... Tufts OpenCourseWare Lecture 7: Inferential Statistics I 2007 Tufts University 1. Introduction Slide 2. Lecture Goals Epidemiology/Biostatistics (J. Forrester) Page - 1 Tufts OpenCourseWare Lecture 7: Inferential Statistics I 2007 Tufts University 3. Lecture Goals, cont. 4. Populations vs. samples 1 Epidemiology/Biostatistics (J. Forrester) Page - 2 Tufts OpenCourseWare Lecture 7: Inferential Statistics I 2007 Tufts University 5. Populations vs. samples 2 6. Some types of samples Epidemiology/Biostatistics (J. Forrester) Page - 3 Tufts OpenCourseWare Lecture 7: Inferential Statistics I 2007 Tufts University 7. Populations vs. samples 3 8. Populations vs. samples 4 Epidemiology/Biostatistics (J. Forrester) Page - 4 Tufts OpenCourseWare Lecture 7: Inferential Statistics I 2007 Tufts University 9. Example of Central Limit Theorem in action 10. Inferential Statistics I: Sampling Distribution - 1 Epidemiology/Biostatistics (J. Forrester) Page - 5 Tufts Op ...

• 9 Pages

#### 194175

Tufts, QA-OCW 194175

Excerpt: ... Tufts OpenCourseWare Lecture 7: Inferential Statistics I 2008 Tufts University 1. Introduction Slide 2. Lecture Goals Epidemiology/Biostatistics (J. Forrester) Page - 1 Tufts OpenCourseWare Lecture 7: Inferential Statistics I 2008 Tufts University 3. Lecture Goals, cont. 4. Populations vs. samples 1 Epidemiology/Biostatistics (J. Forrester) Page - 2 Tufts OpenCourseWare Lecture 7: Inferential Statistics I 2008 Tufts University 5. Populations vs. samples 2 6. Some types of samples Epidemiology/Biostatistics (J. Forrester) Page - 3 Tufts OpenCourseWare Lecture 7: Inferential Statistics I 2008 Tufts University 7. Populations vs. samples 3 8. Populations vs. samples 4 Epidemiology/Biostatistics (J. Forrester) Page - 4 Tufts OpenCourseWare Lecture 7: Inferential Statistics I 2008 Tufts University 9. Example of Central Limit Theorem in action 10. Inferential Statistics I: Sampling Distribution - 1 Epidemiology/Biostatistics (J. Forrester) Page - 5 Tufts Op ...

• 3 Pages

#### Review2

Wisconsin, PSY 225

Excerpt: ... Objectives for Exam 2 Reading Assignments & Lecture Overlap Chapter 5: Inferential Statistics -Making Statistical Decisions What is the purpose of inferential statistics ? How do inferential statistics work? Samples and populations Role of probability in inferential statistics What are the properties of normal distributions. Why is normal distribution important? Understand conceptually the use of the z-test. (Understand the components the go into its calculation Understand the use of the t-test. When is it used and what does it test? What is a null hypothesis? How is it used in inferential statistics ? What is the gamblers fallacy? Chapter 6: Testing the Hypothesis-A Conceptual Introduction What are the three types of variation? How are they important? What are their relations to sampling error, effect sizes and internal validity? What are the steps in hypothesis testing (Understand it, don't just memorize it)? Concepts in hypothesis testing Alpha, Beta p-value Null hypotheses Alternative hypo ...

• 3 Pages

#### wk1lrnobj

Minnesota, PSY 1001

Excerpt: ... dentify and contrast the following measures of central tendency: mean, mode, median. Given a data set, how would you calculate the mean? Mode? Median? 23. Define measures of variability (range and standard deviation) and explain why these measures are important. Inferential statistics 24. Define inferential statistics . 25. Describe the two important conclusions that can be drawn with inferential statistics . 26. Explain what it means when a psychologist states that a finding is statistically significant. Topic 6: Ethical practice in Psychological research 27. Define and identify informed consent, debriefing, and confidentiality. 28. Describe two arguments for and two arguments against animal research. Words, people & concepts to know: Scientific method Hypothesis Operational definition Descriptive research Naturalistic observation Case studies Surveys Psychological testing Reactivity External validity Random sampling Correlational research Correlation coefficient Scatterplot Third variable Experime ...

• 3 Pages

#### 1125L01

Mansfield University of Pennsylvania, MA 1125

Excerpt: ... Math 1125-Introductory Statistics - Lectures 01 8/30/06 1. What is statistics? In statistics, we're trying to make sense of large sets of data. For example, we may have a large database of numbers that we would like to make more palatable. This would be a descriptive statistics task. On the other hand, we may have only partial access to a set of numbers, and we would want to draw conclusions about the entire set. This would be an inferential statistics task. As an example, let's say that your job is to take the SAT scores of all the students applying to Mansfield University in 2006 and to present and compare these to the SAT scores from the previous year. There could be as many as a thousand SAT scores from each year, and our brains really can't handle that much information. Talking about the average SAT score from each year makes the information much easier to take in, and it also makes it easier to make the year-to-year comparison. That's descriptive statistics, and that's great. On the other hand, we've ...

• 16 Pages

#### 194194

Tufts, OCW 194194

Excerpt: ... Tufts OpenCourseWare Lecture 8: Inferential Statistics II 2007 Tufts University 1. Lecture 8 - Introduction Slide 2. Example: Blood Pressure in Medical Students Epidemiology/Biostatistics (A. Tang) Page - 1 Tufts OpenCourseWare Lecture 8: Inferential Statistics II 2007 Tufts University 3. Example, cont. 4. Concepts Illustrated by Example Epidemiology/Biostatistics (A. Tang) Page - 2 Tufts OpenCourseWare Lecture 8: Inferential Statistics II 2007 Tufts University 5. Hypothesis 6. Parameters vs. Statistics Epidemiology/Biostatistics (A. Tang) Page - 3 Tufts OpenCourseWare Lecture 8: Inferential Statistics II 2007 Tufts University 7. Statistic and Parameter 8. Hypothesis Testing and Statistics Epidemiology/Biostatistics (A. Tang) Page - 4 Tufts OpenCourseWare Lecture 8: Inferential Statistics II 2007 Tufts University 9. Skipping ahead&#8230;. 10. P-value Epidemiology/Biostatistics (A. Tang) Page - 5 Tufts OpenCourseWare Lecture 8: Inferential Statistics ...

• 16 Pages

#### 194194

Tufts, QA-OCW 194194

Excerpt: ... Tufts OpenCourseWare Lecture 8: Inferential Statistics II 2008 Tufts University 1. Lecture 8 - Introduction Slide 2. Example: Blood Pressure in Medical Students Epidemiology/Biostatistics (A. Tang) Page - 1 Tufts OpenCourseWare Lecture 8: Inferential Statistics II 2008 Tufts University 3. Example, cont. 4. Concepts Illustrated by Example Epidemiology/Biostatistics (A. Tang) Page - 2 Tufts OpenCourseWare Lecture 8: Inferential Statistics II 2008 Tufts University 5. Hypothesis 6. Parameters vs. Statistics Epidemiology/Biostatistics (A. Tang) Page - 3 Tufts OpenCourseWare Lecture 8: Inferential Statistics II 2008 Tufts University 7. Statistic and Parameter 8. Hypothesis Testing and Statistics Epidemiology/Biostatistics (A. Tang) Page - 4 Tufts OpenCourseWare Lecture 8: Inferential Statistics II 2008 Tufts University 9. Skipping ahead. 10. P-value Epidemiology/Biostatistics (A. Tang) Page - 5 Tufts OpenCourseWare Lecture 8: Inferential Statistics II ...

• 3 Pages

#### sg2

Western Michigan, HOMEPAGES 619

Excerpt: ... SPPA 619 Study Guide for Exam 2: Inferential Statistics Notes: (1) you will need calculator, (2) the exam will cover only the material on inferential statistics , (3) bring the z and t-test tables that were handed out in class. 1. What is the difference between descriptive and inferential statistics ? 2. What kind of theoretical sampling experiment forms the basis of the z test? In your answer, explain the standard error of the mean and tell what factors affect it. 3. What kind of theoretical sampling experiment forms the basis of the t test? In your answer, explain the standard error of the difference between the means. 4. Under what conditions would a z test be used? Answer: When comparing a sample mean to a population mean. 5. Under what conditions would a t test be used? Answer: When comparing two sample means. 6. What are the Null and Alternative Hypotheses? 7. When inferential statistics are used to compare groups, what kinds of errors can occur? Which of these error probabilities can be calculated using ...

• 3 Pages

#### P331 Inferential Statistics (SG)

California PA, P 331

Excerpt: ... Psy 331 Inferential Statistics Key Concepts in Inferential Statistics Key Concepts in Inferential Statistics Assignment: Heiman Chapter 6, review Chapter 9 Terms you should know. Sampling distribution of the mean . . . . . . . . . . . . . . . * Standard error of the mea ...

• 1 Pages

#### 7-2_math112

Walla Walla University, MATH 112

Excerpt: ... Comparing Data Sets The Normal Distribution Paired Data Inferential Statistics Conclusion MATH 112 Section 7.2: Going Beyond the Basics Prof. Jonathan Duncan Walla Walla University Autumn Quarter, 2007 Comparing Data Sets The Normal Distribution Paired Data Inferential Statistics Conclusion Outline 1 Comparing Data Sets The Normal Distribution Paired Data Inferential Statistics Conclusion 2 3 4 5 Comparing Data Sets The Normal Distribution Paired Data Inferential Statistics Conclusion Data Comparison Example Many statistics problems involve comparing two sets of data to determine if they come from similar or different populations. Example Your group's Thanksgiving survey asked respondents how many pieces of pie they eat during the Thanksgiving and Christmas holidays. You broke down your results by gender and found the following: Pieces 0 1 2 3 4 5 6 7 8 9 10 Male 2 1 4 8 5 7 2 3 2 1 2 Female 4 3 2 6 5 3 1 0 1 0 0 Comparing Data Sets The Normal Distribution Paired Data Inferentia ...

• 6 Pages

#### psy321_lecture5

CSU Northridge, PSY 321

Excerpt: ... Outline Research Methods Lecture 5 Analyzing data Scales of measurement nominal, ordinal, interval, ratio Descriptive statistics frequencies, graphs, summary statistics Inferential statistics Statistical hypothesis testing Type I and Type II error Measurement What is measurement? - the assignment of numbers to aspects of objects or events according to a rule or convention (Stevens, 1946) Numbers events, objects Scales of Measurement Nominal scales - assign numbers to events only to classify them into one group or another - property: Nominal Ordinal Interval Ratio e.g., height, weight, personality Scales of Measurement Ordinal scales - measure a variable in order of magnitude - property: Scales of Measurement Interval scales - measure a variable in terms of their order and the distance between the numbers - property: 1 Scales of Measurement Ratio scales - Scales of Measurement Why it is important to know? properties: - provide the best match to the real number system possible to carry out all ...

• 1 Pages

#### Lesson - 23

Western Michigan, ECE 380

Excerpt: ... ECE 3800 Lesson Twenty Three - Confidence Intervals for the Mean Vocabulary: Confidence interval Confidence limits Student's T-distribution Significance level Topics: 1. 2. 3. Finding the confidence interval for a two-tailed distribution Reading the Student-T distribution table Relationship between the Student-T and Gaussian distributions Examples: 1. Find the two-tailed 95% confidence interval for the population mean assuming the following sample data: 62, 55, 64, 56, 58, 54, 49, 52, 65, 70, 61, 46 2. 3. Repeat example (1) assuming only a left-tail. Repeat example (1) assuming only a right-tail Remark: Bring the lectures note entitled " Inferential Statistics Example" to the next lecture. ...

• 3 Pages

#### conBio114_09_philo_studyQuestions

Holy Cross College, CONBIO 114

Excerpt: ... Study Questions: Philosophy of Science and Scientific Methodology and Progress Conservation Biology Spring 2009 1. What is materialism and why must it be seen as an assumption for that matter, what is vitalism what assumptions does it involve? 2. What is the role of observation in formation of inductive generalizations (and what are inductive generalizations) and in experiments? 3. Why should falsifyability and not "prove-ability" be the requirement of a scientific hypothesis? Who was Karl Popper? 4. What causes variation in experimental observations and how does this affect the interpretation of results? Why are inferential statistics needed? What is the role of inferential statistics in hypothesis testing? 5. Why does reductionism stream directly from materialism and why are connections between materialism and emergence more tenuous? What did your instructor mean by "hard and soft emergence?" 6. Describe normal science and contrast it with a scientific revolution. How do scientific paradigms tend to st ...

• 8 Pages

#### P331 Correlation and Regression (SG)

California PA, P 331

Excerpt: ... Psy 331 Inferential Statistics Correlation and Regression Correlation and Regression Assignment: Heiman, Chapters 7 and 8 Terms you should know. Correlation . . . . . . . . . . . . . . . . . ...

• 8 Pages

#### Lecture Outlines-Stat Methods-1.12.2008

Mt. Olive, CRJ 300

Excerpt: ... Statistical Methods for Criminal Justice Lecture Outline for Traditional Program: Purpose of Statistics, Foundations of Research And Data Organization I. Statistics The science of collecting, describing, analyzing, and interpreting observations. A. Statistics is the foundation of the study of crime. 1. II. Therefore, the study of crime necessitates an understanding of statistics. Descriptive and Inferential Statistics A. There are two classes of statistics: descriptive and inferential. 1. 2. Your research goal determines the type of statistics you will use. If your goal is to describe your observations, this can be accomplished by using descriptive statistics. a. 3. Descriptive Statistics It is only when you go beyond listing your observations and begin summarizing your observations in meaningful ways that you need descriptive statistics. If your goal is to move beyond describing your particular sample of observations and make some statements about what is occurring in a given population from which yo ...

• 6 Pages

#### iis

University of Texas, LIS 397

Excerpt: ... INTRODUCTION TO INFERENTIAL STATISTICS A. A contrast . . . 1. Descriptive statistics summarize, display, graph quantitative information 2. On the other hand, inferential statistics , also known as statistical inference A contrast . . . (contd) a. is the process of estimating (population) parameters from (sample) statistics b. or is the process of determining the amount of random error in data, i.e., determining how likely data are by chance alone A contrast . . . (contd) 3. Definitions of inferential statistics a. Spatz (1997, p. 389): a method of reaching conclusions about unmeasurable populations by using sample evidence and probability b. Vogt (1999, p. 277): Using probability and information about samples to draw conclusions (inferences) about a population or about how likely it is that a result could have been obtained by chance. B. Doing inferential statistics 1. Among the most valuable and commonly used inferential techniques are testing hypotheses and generating confiden ...

• 1 Pages

#### Exam 1 Review sheet F08

UMass (Amherst), RESE 212

Excerpt: ... RES EC 212 Exam 1 Review Draft_1! It is my attempt to be helpful, but not to limit what is on the Exam. Road Map to Stats What is Statistics? What is a Statistic? Descriptive and inferential statistics (p. 8, 134 for review terms) What's it all about: Discovery Through Data get a bunch of raw unorganized data, either for a sample or a population you'll organize it and summarize the key information-descriptive stats. If we use sample statistics to infer [estimate] population characteristics (parameters), then inferential stats Populations, samples, statistics, parameters Why Sample? Sampling: convenience sample, simple random sample, stratified random sample, cluster sample, sample bias, sampling error, representative sample. Data; time series, cross-sectional, Likert scale. Observational study vs. a designed experiment. The key is cause and effect. Data, table and picture (visual) summaries of the distribution: (see page 68-69, 88 for Review Terms) From raw data to a summary where we trade off complete det ...

• 2 Pages

#### January 11, 2008

North Dakota, PSYC 241

Excerpt: ... January 11 Outline I. Need for statistics A. Communication-descriptive statistics B. Inferences- inferential statistics 1. Population and parameters 2. Sample and statistics II. Measurement scales A. Nominal: observe differences B. Ordinal: + rank differences C. Interval: + how much more or less D. Ratio: + absolute zero E. Use III. Limits of measurement A. Discrete variable B. Continuous variable Descriptive statistics: concerned with techniques that are used to describe or characterize the obtained data Inferential statistics : involves techniques that use the obtained sample data to infer to populations Population: the complete set of individuals, objects, or scores that the investigator is interested in studying Parameter: a number calculated on population data that quantifies a characteristic of the population Sample: a subset of the population Statistic: a number calculated on sample data that quantifies a characteristic of the sample Variable: any property or characteristic of some event, object, o ...

• 1 Pages

#### Inferentialstatistics

Clemson, LAPTOP 301

Excerpt: ... Inferential Statistics is concerned with making inferences (predictions or decisions) about a population based on the information contained in a sample. ( Inferential statistics describes how things probably will be.) ...