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18 Cards in this Set
- Front
- Back
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Falsifiability Principle
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For any hypothesis to have credence, it must be inherently disprovable before it can be accepted as a scientific hypothesis or theory
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5 Principles of Science
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Empiricism Scepticism Openness Tentativeness Independence from authority |
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Goals of Science
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Describe Explain Predict Control |
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Independent Variable
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The variable independely manipulated by the experimenter. Also: factor, explanatory/predictor variable
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Dependent Variable
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A measure resulting from changes in the IV or another DV
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Transformation
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A mathematical operation that apply to every score in the data set
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Linear transformation
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Doesn't change the shape of the overall distribution, but can change the value for the mean, median and mode
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Z-score
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An example of transformation The number of standard deviations the score is above or below the mean (z=0 is equal to the mean for that variable) Transformation of raw scores into standardised scores |
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Z-score distributions
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(1) the mean of all the z-score in a distribution is always 0 (2) the standard deviation of all the z-scores in a distribution is always 1 (3) the shape of the distribution of z-scores is identical in shape to the distribution of the original raw scores |
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Normal distribution - why?
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(1) many variables naturally occur as normal distributions (2) many inferential statistics were developed assuming normally distributed population |
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68-95-99.7 rule
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For normal distributions: 68% of values are between +1 and -1 σ from μ 95% of values are between +2 and -2 σ from μ 99.7% of values are between +3 and -3 σ from μ |
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Random Sampling
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A random sample is one selected from a population such that each individual has an equal and independent chance of being selected
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Sampling Error
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The statistics of randomly drawn samples will deviate from the corresponding population parameters
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Random Sampling Variability
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Owing to chance two random samples from the same population will have different statistics
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Central Limit Theorem
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As the sample size increases, the sampling distribution of the means becomes more normal (even apply when the original population of raw scores is not normally distributed) |
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Properties of the Sampling Distribution of the Means
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(1) the mean of the distribution of means is equal to the mean of the population of individual scores (2) the variance of the distribution of means is equal to the variance of the population scores divided by the sample size (3) the resulting shape of the distribution is normal if the original population was normal or if not, larger sample size |
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Five steps of hypothesis testing
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(1) make some statements about the population parameters: a null H and an alternative H (2) determine the population parameters assuming the null hypothesis is true (3) determine a "cut-off" point where the null H should be rejected (4) determine the probability of your sample statistic assuming the null hypothesis is true (5) if exceeds the cut-off point, reject. Otherwise, retain |
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