Download WHY STATISTICS? and more Lecture notes Statistics in PDF only on Docsity!
WHY STATISTICS?
- Methods of Knowing TOPIC SLIDE
- The Scientific Method
- Independent versus Dependent Variables
- Qualitative Data versus Quantitative Data
- Discrete Data versus Continuous Data
- Scales of Data: Nominal, Ordinal, Interval, and Ratio
- Descriptive Statistics versus Inferential Statistics
- Populations versus Samples
➊ Authority
➋ Rationalism
➌ Intuition
➍ The scientific method
WHY STATISTICS?
➊ The rules of logic are used to analyze information and deduce what is true and what is not
WHY STATISTICS?
- Example: Syllogism
- All stats instructors are interesting
- Mr. X is a stats instructor
- Therefore, Mr. X is interesting
- Can lead to wrong conclusions and inaccurate generalizations
➊ The sudden, often clarifying idea that springs into consciousness (not in parts but as a whole)
WHY STATISTICS?
- Example: While watching a movie, the solution to a problem you were working on earlier suddenly comes to mind as if someone just flipped a switch
➊ Defining the problem to be studied and the questions to be answered ➋ Specifying the null and alternative hypotheses ➌ Operationally defining the
- Independent variable (IV) and its levels
- The researcher changes the type or amount of this variable from one group to the next
- Dependent variable (DV)
- The data collected by the researcher
- The data represent the amount observed
THE SCIENTIFIC METHOD
➊ Trying to determine if changes in the IV are associated with changes in the DV
- Example:
- One group takes a new sleeping medication while a second group takes no medication.
- The researcher might compare the average time it takes each group to fall asleep.
- The IV is the amount of sleep medication each group takes. There are two levels – the group that takes the medication and the group that doesn’t
- The DV is the average amount of time it takes each group to fall asleep (measured in minutes)
The Scientific Method
➊ Enables the researcher to:
- Draw conclusions about a population based on the results from a sample from the population
- Example: The news media draw conclusions about which political candidates will win an election from surveys based on only a fraction of the population (usually around 1500 likely voters)
SO THEN WHY STATISTICS?
➊ Enables the researcher to:
- Understand the difference between results that reflect chance error from results that indicate a real effect
- Example: A coin is flipped 10 times. We expect 5 heads and 5 tails but we observe seven heads and three tails. Is this outcome due to chance or due to a biased (unfair) coin?
SO THEN WHY STATISTICS?
➊ What is observed ➋ What has been measured ➌ The numbers entered into a statistics problem ➍ The dependent variable (DV)
- EXAMPLES: Heart rate (BPM), Average life of a mechanical part (in days), Amount of fruit yielded per acre (in pounds), Number of correct items on a test
WHAT ARE DATA?
➊ Describe a quality or characteristic using non-numeric labels
- EXAMPLES:
- Slow, average, fast
- Small, medium, large
- Poor, good, excellent
- Close, nearby, far away
- Lazy, acceptable, efficient
- None, some, many
TYPES OF DATA: QUALITATIVE VS. QUANTITATIVE
➊ Numeric data that have no intervals between values
- These are non-decimal or whole values
- EXAMPLES:
- Number of unemployment claims
- Degrees awarded last semester
- Total number of citations issued last month
- Gender
- Number of cars sold last month
- Total number of homeruns hit last season
TYPES OF DATA: DISCRETE VS. CONTINUOUS
➊ Numeric data with intervals between values
- These can be decimal or fraction values
- EXAMPLES:
- Fever measured in Fahrenheit
- Price of a gallon of gas
- Rate of inflation per year
- Amount of weight lost (in pounds and ounces)
- Olympic time records (hundredths of a second)
- Distance traveled (meters, millimeters)
TYPES OF DATA: DISCRETE VS. CONTINUOUS
➊ Describe which category or group the participant belongs to ➋ Provide information as to how many participants are in each group/category
- EXAMPLE: Favorite brand of athletic shoes
- EXAMPLES: Gender, Year in school, County of residence, Species of trees observed, political party
SCALES OF DATA: NOMINAL SCALE
Reebok Nike LA Gear Addidas Wilson
IIII IIII IIII IIII II IIII IIII II III
➊ Describe which magnitude or rank ➋ Provide information as to which scores rank smaller and which rank larger
- EXAMPLES:
- Place finished in a race (i.e., 1 st , 2 nd , 3 rd )
- A rank order of Macy’s top selling stores across the country
- Year in school (i.e., freshman, sophomore, etc.)
- The floors of an apartment building
- A rank order of the top paying jobs in nursing
SCALES OF DATA: ORDINAL SCALE
