Statistics: the science of planning studies and experiments, obtaining data, and the organizing, summarizing.

Population: the complete collection of all measurements or data that are being considered

Census vs. Sample

Census: collection of data from every member of a population Sample: Sub collection of members selected from a population

Section 1.2 Potential Pitfall- Misleading Conclusions

Concluding that one variable causes the other variable when in fact the variables are only correlated or associated together

Potential Pitfalls – Small Samples: Conclusions should not be based on samples that are far too small

Potential Pitfall- loaded Questions: If survey questions are not worded carefully the results of a study can be misleading

Potential Pitfalls- Non response: Occurs when someone either refuses to respond to a survey question or is

Potential Pitfall- Missing Data: can dramatically affect results, subjects may drop out for reasons unrelated to the study

Example- people with low income are less likely to report than wealthy

Potential Pitfall- Precise Numbers: Because as a figure

Potential Pitfalls- Percentages: Misleading or unclear percentages are sometimes used

Examples- contential airlines ran an ad claiming “We’ve already improved 100% with losing six months

Section 1.3 Parameter: A numerical measurement describing some characteristics of a POPULATION (doesn’t change)

POPULATION=PARAMETER

Statistic: A numerical measurement describing some characteristic of a SAMPLE (sample is unique)

SAMPLE=STATISTIC

Quantitative Data- Quantative (or Numerical) data- consists of numbers representing counts or measurements

Categorical Data - Categorical (or qualitative or attribute Data) – consists of names or labels representing categories Ex: the gender (male/female) are student athletes

Result from infinitely many possible values that correspond to some continuous scale that cover a range of values without gaps, interruption, or jumps

Nominal Level of measurement characterized by data that consists of names labels, or categories only, and the data cannot be arranged in an ordering scheme

Ordinal level of measurement involves data that can be arranged in some order but differences between data values either cannot be determined or are meaningless ex: course grades A, B, C, D, or F

Interval Level of measurement involves data that can be arranged in order and the difference between any two data values is meaningful. However there is no natural zero starting point (where none of the quantity is present) Ex. Years 1000, 2000, 1776, and 1492

Ratio level of measurement the interval level with the additional property that there is also a natural zero starting point (where zero indicates that none of the quantity is present); for values at this level, differences and ratios are meaningful Ex. Colors of m&ms listed in data set 20 – Nominal

Depths of earthquakes listed data set 16- ratio

The movie avatar was given a rating of 4 stars out of 5 ordinal?

Summary Levels of Measurement

Nominal- categories only

Ordinal- categories with some order

Interval- differences but no natural zero point

Ratio- differences and a natural zero point

Section 1.4 Observational Study- observing and measuring specific characteristics without attempting to modify the subjects being studied

Experiment- apply some treatment and then observe its effects on the subjects (subjects in experiments are called experimental units) Ex: The pew research center surveyed 2252 adults and found that 59% of them go online wireless

In the largest public health experiment ever conducted 200,745 children were given a placebo

Simple Random Sample- a sample of n subjects is selected in such a way that every possible sample

Random Sampling- members from the population are selected in such a way that each individual