information Descriptive statistics organize, summarize, and communicate a group of numerical observations and describe large amounts of data in a single number or in just a few numbers Inferential statistics
Use samples to draw conclusions about a population Inferential statistical use sample data to make general estimates about the larger population, and infer or make an intelligence guess about, the population Sample: a set of observations drawn from the population of interest. Samples are used most often because it is rare that we are able to study every person (or organization or laboratory rat) in a particular population. Researchers usually study a sample, but they are really interested in the population, which includes all possible observations about which we’d like to know something. Discrete Variables that can only take on specific values (e.g., whole numbers) How many letters are in your name? * Continuous Can take on a full range of values, How tall are you? Variables are observations of physical, attitudinal, and behavioral characteristics that can take on different values. We use both discrete and continuous numerical observations to quantify variables. Discrete observations can take on only specific values (e.g., whole numbers); no other values can exist between these numbers. Continuous observations can take on a full range of values (e.g., numbers out to several decimal places); an infinite number of potential values exists. Two types of observations are always discrete: nominal and ordinal variables. Two types of observations that can be continuous are interval variables and ratio variables. Interval variables are used for observations that have numbers as their values; the distance (or interval) between pairs of consecutive numbers is assumed to be equal. For examples, time is an interval variable because the interval from one second to the next is always the same. Some interval variables are also discrete variables, such as the number of times one has to get up early each week. This is an interval variable because the distance between numerical observations is assumed to be equal. The difference between 1 and 2 times is the same as the difference between 5 and 6 times. However, this observation is also discrete because, as noted earlier, the number of days in a week cannot be anything but whole numbers. Several social science measures are treated as interval measures but also are discrete, such as personality and attitude measures. Studies that measure time and distance are continuous, interval observations. Ratio variables are variables that meet the criteria for interval variables but also have meaningful zero points. A scale variable is a variable that meets the criteria for an interval variable or a ratio variable. Scale: Nominal Characteristics: Label and categorize; No quantitative distinctions Examples: Gender; Diagnosis; Experimental or Control Scale: Ordinal Characteristics: Categorizes observations Categories organized by size or magnitude Examples: Rank in class; Clothing sizes (S,M,L,XL); Olympic medals Scale: Interval Characteristics: Ordered categories; Interval between categories of equal size; Arbitrary or absent zero point Example: Temperature; IQ; Golf scores (above/below par) Scale: Ratio Characteristics: Ordered categories; Equal interval between categories; Absolute zero point Examples: Number of correct answers; Time to complete task; Gain in height since last year
Reliability Consistency of the measure. Validity Extent the test measures what it is supposed to measure A reliable measure is one that is consistent. But a reliable measure is not necessarily a valid measure. A valid measure is one that measures what it was intended to measure. Any test with poor reliability cannot have high validity. Conducting Experiments to Control for Confounding Variables Correlation An association between two or more variables Experiment A study in which...
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