Understanding Statistics: Key Concepts and Definitions
Learn about important statistical concepts such as population vs. sample, parameter vs. statistic, descriptive vs. inferential statistics, random selection and assignment, and internal vs. external validity. Get insights into variables and scale types in this comprehensive guide.
Download Presentation
Please find below an Image/Link to download the presentation.
The content on the website is provided AS IS for your information and personal use only. It may not be sold, licensed, or shared on other websites without obtaining consent from the author. If you encounter any issues during the download, it is possible that the publisher has removed the file from their server.
You are allowed to download the files provided on this website for personal or commercial use, subject to the condition that they are used lawfully. All files are the property of their respective owners.
The content on the website is provided AS IS for your information and personal use only. It may not be sold, licensed, or shared on other websites without obtaining consent from the author.
E N D
Presentation Transcript
Questions Define and illustrate: Population, Sample Parameter, Statistic Descriptive, inferential statistics Random selection (sampling), assignment Internal, External validity Discrete, continuous variables Scale types (nominal, ordinal, interval, ratio)
Population vs. Sample Population collection of all the objects of interest to researcher (you). College students, students at USF Sample subset of objects from the population Want a representative sample Samples are relatively practical Random samples have good properties One person s sample is another s population
Parameter vs. Statistic Parameter numerical summary of population E.g., mean, standard deviation Statistic numerical summary of sample E.g., mean, standard deviation Typically we compute statistics and estimate parameters using statistics.
Descriptive vs. Inferential Descriptive statistics describe a sample How tall are these students? Inferential statistics use sample statistics to make decisions about populations. Is one method of instruction better than another?
Random Select & Assign Random selection is a process of picking a sample from a population so that each element has the same probability of being sampled. E.g., lottery, every 3rdname from a list (this is actually a systematic sample but it s good) Random assignment is assignment to treatment so that each element has an equal probability of being assigned to each treatment. E.g., lottery, every other name, etc. Both are typically accomplished by lists (aka frames) and computer generated numbers (e.g., SAS PROC PLAN)
Internal, External Validity Internal validity - quality of inferences about the study itself. Random assignment, history, maturation, etc. External validity quality of inferences from the study to the larger domain of interest. Representative sample of participants, task relevance, behavioral consequents, etc. Aka generalizability of the results (but not generalizability study).
Variable & Distribution Variable vs. constant Attribute either varies across objects or not Distribution: Collection of data Distribution: Array of scores Height Beck Depression Index Rat bar press Wonderlic
Discrete vs. Continuous Math Integer vs. real numbers Data Categorical vs. continuous (many valued, ordered) Examples Political party, job satisfaction, response time, country of origin
Scale types Nominal, ordinal, interval, ratio Nominal categories. No ordering; mean has no connection to attributes Ordinal rank order only Interval rank order plus equal interval. ratio of differences has meaning Ratio rank order, equal intervals, rational zero point. Ratio of numbers has meaning.
Scale Types: Footrace review Nominal Ordinal Interval Ratio ID number Rank order of finish Time of day of finish 10:57 a.m. Elapsed time from start 4 min 043 1 011 2 10.59 a.m. 6 min 136 3 11:01 a.m. 8 min 112 4 11:02 a.m. 9 min 086 5 11:04 a.m. 11 min
Review Find a partner to work on this exercise. Suppose you want to know whether one brand of tennis shoe is better than another. You have about $10K from a grant to study this. Describe a study you might conduct to find out. What might be your population, sample, independent and dependent variables? What statistics might you want to compute? Never mind the actual statistical test at this point. What data would you gather? What might a critic say about the internal and external validity of your study? What scale types are your IV and DV?
