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Chapter 1
Statistics, Data, and Statistical Thinking
The Science of Statistics
Statistics – the science of data
Collection E l ti ( l ifi ti i ti
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Evaluation (classification, summary, organization and analysis ) Interpretation
Types of Statistical Applications
Descriptive Statistics - describe collected data
“Nearly 87% of players participating in
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Nearly 87% of players participating in a Speed Training Program improved their sprint times.”
“Only about 3% of players participating in a Speed Training Program had decreased times.”
Types of Statistical Applications
Inferential Statistics - make generalizations about a group based on a subset (Sample) of that group
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“Based on exit polls, more people voted for Candidate A.”
Fundamental Elements of Statistics
Experimental Unit – object of interest example – graduating senior Population – the set of units we are i t t d i l i b t
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interested in learning about example – all 1450 graduating seniors at “State U” Variable – characteristic of an individual population unit example – age at graduation
Fundamental Elements of Statistics
Sample – subset of population example – 100 graduating seniors at “State U ” Statistical Inference – generalization about a
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population based on sample data example – The average age at graduation is 21. (based on sample of 100) Measure of reliability – statement about the uncertainty associated with an inference
Fundamental Elements of Statistics
Elements of Descriptive Statistical Problems
- population/sample of interest
- investigative variables numerical summary tools (charts graphs tables)
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- numerical summary tools (charts, graphs, tables)
- pattern identification in data
Fundamental Elements of Statistics
Elements of Inferential Statistical Problems –population of interest –investigative variables sample taken from population
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–sample taken from population –inference about population based on sample data –Reliability measure for the inference
Types of Data
Quantitative Data
•measured on a naturally occurring scale •equal intervals along scale (allows for
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meaningful mathematical calculations) •data with absolute zero (zero means no value) is ratio data (bank balance, grade) •Data with relative zero (zero has value) is interval data (temperature)
Types of Data
Qualitative Data •measured by classification only •Non-numerical in nature
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•Meaningfully ordered categories identify ordinal data (best to worst ranking, age categories) •Categories without a meaningful order identify nominal data (political affiliation, industry classification, ethnic/cultural groups)
Types of Data
•Different statistical techniques used for quantitative and qualitative data •Qualitative and Quantitative data can be
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used together in some techniques •Quantitative data can be transformed into Qualitative data through category creation •Qualitative data cannot be meaningfully transformed into Quantitative data
Collecting Data
•Data Sources –Published source – books, journals, abstracts •Primary vs. secondary –Designed Experiment
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Designed Experiment •Often used for gathering information about an intervention –Survey •Data gathered through questions from a sample of people –Observational Study •Data gathered through observation, no interaction with units
Summary
- Sources of Error in Survey Data
- selection bias
- non-response bias
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