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| Draw a sample from a larger population to represent the whole population. Put names in a hat.
Make sure every member/element has equal chance of being selected. |
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| 1/2 of random sample goes to treatment group and the other 1/2 goes to the control group. |
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| entire collection of events (scores, incomes, speeds, etc.) that you’re studying |
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| small number from larger population. Allows us to infer something about characteristics of population. |
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1st aspect of randomness. Does the sample reflect the population? Ex.
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Small town from NE wouldn’t rep US hispanic culture well. |
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| 2nd aspect of randomness. After the subjects have been selected, subjects then have to be randomly assigned to treatment or control. |
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| Are the results the result of the differences in the way we treated our groups (hope so) and not a result of WHO we placed in each group. |
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| property of an object/event that can take on different values (hair color, self-confidence, gender, personal control, treatment groups) |
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| Researchers decide what these will be (group memberships like gender groups or teaching style, etc.) |
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| Researchers have NO control over these. (resulting self-esteem scores, personal control, etc.
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| limited number of values (gender, high school grades) |
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| any value between lowest and highest points on a scale (age, self-esteem) |
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| aka. Measurement Data.Numerical data (weights, test scores) other “how much” tests |
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| aka.
Quantitative Data. Numerical data (weights, test scores) other “how much” tests |
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| aka. Qualitative/Frequency Data. (no numbers) |
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| aka. Qualitative/Categorical Data. (no numbers) |
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| aka.
Categorical/Frequency Data. (no numbers) |
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| Describing a set of data (means, graphs, extreme scores, oddly shaped distributions) |
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| Exploratory Data Analysis (EDA) |
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| Joh Tukey showed necessity of paying close attention to examining data in close detail before invoking more tech involved procedures |
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| do after descriptive statistics, after we have a basic understanding of the numbers. |
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| a measure that refers to an entire population (average self-esteem score) |
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| same measure as a parameter, calculated from sample of data we have collected. |
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| labels categorical data.
(gender, political parties) |
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| simplest true scale. Orders people, objects or events along a continuum (ranks in military) |
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| allows us to speak of differences between scale points (same different between 10-15 degree C as there is between 15-20 degree C) |
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| has a true 0 point (true absence). Allows us to speak of ratios/fractions. (length, volume, time) |
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