Chapter 1 Getting Started Understanding Basic Statistics Fifth Edition By Brase and Brase Prepared by Jon Booze

© Cengage Learning. All rights reserved.1 | 2 What is Statistics? Collecting data Organizing data Analyzing data Interpreting data

© Cengage Learning. All rights reserved.1 | 3 Individuals and Variables Individuals are people or objects included in the study. Variables are characteristics of the individual to be measured or observed.

© Cengage Learning. All rights reserved.1 | 4 Variables Quantitative Variable – The variable is numerical, so operations such as adding and averaging make sense. Qualitative Variable – The variable describes an individual through grouping or categorization.

© Cengage Learning. All rights reserved.1 | 5 Variables Quantitative Variable – The variable is numerical, so operations such as adding and averaging make sense. Qualitative Variable – The variable describes an individual through grouping or categorization. Which of the following is an example of a qualitative variable? a). Ageb). Mass c). Religious preference d). Batting average

© Cengage Learning. All rights reserved.1 | 6 Variables Quantitative Variable – The variable is numerical, so operations such as adding and averaging make sense. Qualitative Variable – The variable describes an individual through grouping or categorization. Which of the following is an example of a qualitative variable? a). Ageb). Mass c). Religious preference d). Batting average

© Cengage Learning. All rights reserved.1 | 7 Data Population Data – The data are from every individual of interest. Sample Data – The data are from only some of the individuals of interest.

© Cengage Learning. All rights reserved.1 | 8 Data Which of the following Venn diagrams shows the relationship between population data and sample data? a).b). c).d). SP S PS P P S

© Cengage Learning. All rights reserved.1 | 9 Data Which of the following Venn diagrams shows the relationship between population data and sample data? a).b). c).d). SP S PS P P S

© Cengage Learning. All rights reserved.1 | 10 Levels of Measurement Nominal Level – The data consists of names, labels, or categories. Ordinal Level – The data can be ordered, but the differences between data values are meaningless.

© Cengage Learning. All rights reserved.1 | 11 Levels of Measurement Interval Level – The data can be ordered and the differences between data values are meaningful. Ratio Level – The data can be ordered, differences and ratios are meaningful, and there is a meaningful zero value.

© Cengage Learning. All rights reserved.1 | 12 Levels of Measurement The freezing points of four liquids are 32°F, 6°F, 13°F, and 20°F. What is the level of these measurements? a). Nominal b). Ordinal c). Interval d). Ratio

© Cengage Learning. All rights reserved.1 | 13 Levels of Measurement The freezing points of four liquids are 32°F, 6°F, 13°F, and 20°F. What is the level of these measurements? a). Nominal b). Ordinal c). Interval d). Ratio

© Cengage Learning. All rights reserved.1 | 14 Critical Thinking Reliable statistical conclusions require reliable data. When selecting a variable to measure, specify the process and requirement for the measurement. Pay attention to the measurement instrument and the level of measurement. Are the data from a sample or from the entire population?

© Cengage Learning. All rights reserved.1 | 15 Two Branches of Statistics Descriptive Statistics: Organizing, summarizing, and graphing information from populations or samples. Inferential Statistics: Using information from a sample to draw conclusions about a population.

© Cengage Learning. All rights reserved.1 | 16 Sampling Techniques Simple Random Sampling, Sample size = n –Each member of the population has an equal chance of being selected. –Each sample of size n has an equal chance of being selected. Stratified sampling Population Subgroup 4 Subgroup 1 Subgroup 2 Subgroup 3 sample

© Cengage Learning. All rights reserved.1 | 17 Sampling Techniques Systematic sampling –Number every member of the population. –Select every kth member. Cluster sampling –Population is naturally divided into pre- existing segments. –Make a random selection of clusters, then select all members of each cluster. Convenience sampling - Collect sample data from a readily-available population database.

© Cengage Learning. All rights reserved.1 | 18 Critical Thinking Sampling frame – a list of individuals from which a sample is selected. Undercoverage – resulting from omitting population members from the sample frame. Sampling error – difference between measurements from a sample and that from the population. Nonsampling error – result of poor sample design, sloppy data collection, faulty measuring instruments, bias in questionnaires, and so on.

© Cengage Learning. All rights reserved.1 | 19 Critical Thinking Which of the following sampling strategies is likely to lead to a non-sampling error? Individuals are selected at random from… a). A database of social security numbers. b). A cluster of phone books. c). A collection of birth certificates. d). None of these is likely to introduce non- sampling error.

© Cengage Learning. All rights reserved.1 | 20 Critical Thinking Which of the following sampling strategies is likely to lead to a non-sampling error? Individuals are selected at random from… a). A database of social security numbers. b). A cluster of phone books. c). A collection of birth certificates. d). None of these is likely to introduce non- sampling error. Not everyone has a phone. Sampling from phone books may introduce bias.

© Cengage Learning. All rights reserved.1 | 21 Guidelines For Planning a Statistical Study 1.Identify individuals or objects of interest. 2.Specify the variables. 3.Determine if you will use the entire population. If not, determine an appropriate sampling method 4.Determine a data collection plan, addressing privacy, ethics, and confidentiality if necessary.

© Cengage Learning. All rights reserved.1 | 22 Guidelines For Planning a Statistical Study 5.Collect data. 6.Analyze the data using appropriate statistical methods. 7.Note any concerns about the data and recommend any remedies for further studies.

© Cengage Learning. All rights reserved.1 | 23 Census vs. Sample In a census, measurements or observations are obtained from the entire population (uncommon and often impractical). In a sample, measurements or observations are obtained from part of the population (common).

© Cengage Learning. All rights reserved.1 | 24 Observational Studies and Experiments Observational Study – Measurements are obtained in a way that does not change the response or the variable being measured. (No treatment is applied.) Experiment – A treatment is applied in order to observe its effect on the variable being measured.

© Cengage Learning. All rights reserved.1 | 25 Experiment Used to determine the effect of a treatment. Experimental design needs to control for other possible causes of the effect. –Placebo effect. –Lurking variables. To minimize these confounds, create one or more control groups that receive no treatment.

© Cengage Learning. All rights reserved.1 | 26 Experiment Designs Blocking –A block is a group of individuals with some common characteristic that might affect the treatment. –A randomized block design randomly assigns each block member to a treatment. –Used to control suspected lurking variables. Randomization – A random process is used to assign individuals to a treatment group or to a control group.

© Cengage Learning. All rights reserved.1 | 27 Experiment Designs Double-Blinding – minimizes the unintentional transfer of bias between researcher and subject.

© Cengage Learning. All rights reserved.1 | 28 Surveys Collecting data from respondents by asking them questions. Survey Pitfalls Nonresponse → undercoverage of population. Truthfulness – respondents sometimes lie. Faulty recall of respondent Hidden bias – due to poor question wording. Vague wording – “sometimes”, “often”, “seldom” Interviewer influence – who is asking the questions and in what manner. Voluntary response – relatively interested individuals are more likely to participate.