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Stats Ch 1 & 2

Descriptive Statistics organizes, summarizes (or describes) and displays data
Inferential Statistics Analyzes, interprets, and draws conclusions about a larger unobserved population.
Population the complete collection of elements (scores, people, etc) to be studied
Sample sub-collection of elements from the population (must be unbiased and random)
Census a collection of data from every element in a population
parameter a numerical measurement describing some characteristic of a population
statistic a numerical measurement describing some characteristic of a sample
sampling error not an error we make; its just different because we use a sample instead of the whole population
non-sampling error human error (wrong calculation, bad sample)
Quantitative Data data which consists of numbers representing counts or measurements
Qualitative (categorical) data data that can be separated into different categories by some non-numerical characteristic
Examples of Quantitative Data # of kids, height, weight, age, shoe size
Examples of Qualitative Data Gender, Hair color, social security #, area code
Discrete Data Data which represents counts. Whole number data (0, 1, 2...)
Continuous Data Data which represents measurements (decimals, fractions, etc)
Ex. of Discrete Data # of kids
Ex. of Continuous Data height, weight, age, shoe size
4 Levels of Measurement for Data Nominal, Ordinal, Interval, Ratio
2 Types of Qualitative Measurements for Data Nominal, Ordinal
2 Types of Quanitative Measurements for Data Interval, Ratio
Nominal Level characterized by data that consists of names, labels, or categories only. Can't order this data, can not be used for calculations. *qualitative*
Ordinal Level Data we can arrange in some rank order. Difference between each data pieces cannot be determined or are meaningless. These values are generally not used for calculations, GPA is an exception. *qualitative*
Interval Level This is like ordinal, but now meaningful differences between each data piece does exist.. There is no natural starting point, never a time when none of the quantity is present. *quanitative*
Ratio Level This is like Interval but has an inherent zero starting point. Differences and ratios are meaningful, therefore used for calculations. *Quanitative*
Ex. of nominal hair color, gender, ss #, area code, zip codes, eye color, jersey numbers
Ex. of ordinal movie ratings, military ranks, CEO/workers, class titles (fresh, soph...), letter grade
Ex. of interval Clock time, particular year, temperature
Ex. of ratio weight, height, age, #of years, # of kids
Observational Study observe and measure specific characteristics, but we don't attempt to modify the subjects being studied
Experiment apply a treatment and then observe its effects on the subjects (something has to be manipulated)
Simple Random Samples "n" subjects are selected in such a way that every possible sample of size "n" has the same chance of being chosen (every group has an equal chance)
Random Sample Each individual of the group has an equal chance of being selected
Systematic Sampling select a starting point and then select every "k"th element in the population (every 10th person, every 12th person)
convenience sampling use results that are readily available (usually not a good sample)
stratified sampling form groups, take some people frome each group
cluster sampling form groups, randomly select groups, and use ALL members from the selected groups
the "error" the numerical difference between the sample result and the true population result
placebo effect an untreated subject incorrectly believes that he or she is receiving a treatment and reports an improvement in symptoms
blind experiment the subject doesnt know is he/she is getting the treatment or the placebo
double blind neither the subjects nor the evaluators are aware of who got the treatment or the placebo
Created by: hroyal