correlation ≠ causation 1.2 Intro into Statistics Notes Statistics - the science of planning studies & experimenting, obtaining data & organizing data. • Voluntary response often has bias Statistical significance is achieved in a study when we get a result that is very unlikely to occur. Potential pitfalls • Misleading conclusions • Nonresponse • Small samples • Missing data • Loaded questions • Precise numbers • Order of questions • Percentages Practical significance - practical implication 1-3 Types of Data Quantitative data- #s representing counts or measurements Discrete data- when the data values are quantitative & the # of values are countable (numerical data- results from infinitely many possible quantitative values, is not countable). Continuous (numerical) data- results from infinitely many possible quantitative values, is not countable. Categorical data- names or labels that are not #s representing counts or measurements Statistical Inference: Data at hand -> population at large Parameter- # that describes an aspect or attribute of a model for a population. Statistic- a value computed from data only using any unknown parameters. Levels of Measurement Nominal level- characterized by data that consists of names, labels, or categories only. The data cannot be arranged in some order. Ex: Survey responses yes, no, undecided Ordinal level- 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,F) Interval level- data that can be arranged in order & the difference between any two data values is meaningful; no natural zero Ex: Years 1492 Ratio level- the additional property that there is also a natural zero Ex: Price of textbooks percentage frequency = f ofall f ofclass x 100% Gaps indicate samples from two diff. pops. Frequency - the # of original values that fall into that class (6) 6.0 ≈ #ofclasses 49.9−11.7 ≈ 5

Types of Data Statistics

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