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50 Cards in this Set
- Front
- Back
A value, usually unknown (and which therefore has to be estimated) used to represent a certain population characteristic ex. the mean ages |
Parameter |
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Quantity calculated from a sample of data. Gives info about values in the corresponding population. |
Statistic |
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2 basic types of inferential statistics |
estimation statistics & hypothesis testing |
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Used to make statistical inferences about the data. Based on statistical results, researcher can make an educated assumption concerning what the data means. |
Inferential statistics |
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2 types of estimation statistics |
Confidence intervals & Parameter Estimation |
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Used to describe or summarize data - means - ranges of scores - percentiles |
Descriptive statistics |
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Deal w/ the surety that the researcher has that the sample parameter is representative of the population parameter |
Confidence intervals |
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Used to make inferences about how well a particular model might describe the relationship between variables in a population ex. regression analysis |
Parameter estimation |
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A single independent variable is used to predict the value of a dependent variable |
Simple Linear Regression |
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2 or more independent variables are used to predict the value of a dependent variable |
Multiple Linear Regression |
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Deals w/ the relationship between a set of independent variables and a dependent variable |
Nonlinear Regression |
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Similar to techniques like ANOVA & multiple regression. Allows the researcher to specify the nature of the relationship. |
Nonlinear Regression |
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The hypothesis of no change or effect Ho ex. there us no difference between those who receive a kind of speech therapy and those who don't. |
Null hypothesis |
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The hypothesis of change or effect H1 The RESEARCH hypothesis, states what researcher is looking for ex. there is a difference between those who receive some kind of speech therapy and those who don't |
Alternate hypothesis |
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Rejecting the null hypothesis when in fact it was true. (We say therapy made a difference when it really didn't) |
Type I Error |
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Failing to reject the null hypothesis when it is actually false. (We say the therapy didn't make a difference when it really did) |
Type II Error |
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A probability, w/ a value ranging from zero to 1. |
P-value |
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A measure of how much evidence we have against the null hypothesis |
P-value |
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Calculated to determine how secure researcher can be that the sample results do not reflect the population parameter |
P-value |
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Rejecting the null hypothesis says that research results ______ _ ________. |
showed a difference |
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Deals w/ the concept that research results are due to chance |
P-value |
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______ p-value means results observed unlikely to be due to chance. (What you are looking at really did result in a difference) |
Small |
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_______ p-value means there is a strong possibility that the results are due to chance. |
Large |
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Predetermined acceptance level for the p-value Not calculated |
Alpha level |
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Chosen by researcher prior to beginning of study &/or looking at the statistics - can be any number between 0 & 1 |
Alpha level |
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Alpha level is typically ___. |
.05 |
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Used to determine if results are statistically significant |
Alpha level |
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If statistics _____ alpha level, results are significant. |
meet |
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If statistics _____ alpha level, results are not significant. |
don't meet |
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______ level is aka ______ ______ aka the probability of a ____ ___ error. |
Alpha level, significance level, aka type II error |
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Used to compare difference between 2 proportions Categorical data |
z-test |
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Used to compare difference between 2 means Numerical data |
t-test |
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Used to compare independent groups |
Pooled variance t-test |
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Used to compare related groups -matched according to relevant characteristics -repeated measures |
Paired t-test |
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Used to compare differences in proportions between 2 or more groups categorical data |
Chi-square tests |
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Tests for -the difference in proportion of successes in 2 or more groups -a relationship between 2 categorical variables in a 2 way cross classification table |
Chi-square tests |
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Analysis of variance used to compare difference between means of more than 2 groups numerical data |
One-Way ANOVA |
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Tests for differences among means of more than 2 groups Simultaneously compares the difference among the means of more than 1 group. Doesn't really test variance |
One-Way ANOVA |
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DIRECTIONAL hypothesis is used when either only positive or negative differences are of interest in an experimental study and you would use a ____ _____ test. |
one-tailed test |
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NONDIRECTIONAL hypothesis is used to distinguish between no effect and an effect in the unexpected direction and a ___ _____ test would be used. |
two-tailed test |
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_______ _______ are used to allow researcher to rule out chance as an explanation for the results observed. |
Statistical tests |
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A main result of a correlation is called the ________ ________. or "r" |
Correlation coefficient (r) |
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r= +1 means that every time one variable gets larger, the other _______ _____. This is a _______ positive correlation and does not typically happen. |
does too, perfect |
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r= -1 means every time one variable gets larger, the other ______ ______. A ______ negative correlation also does not typically happen. |
gets smaller, perfect |
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Only qualitative classifications - gender - race |
Nominal Variables |
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Can measure in terms of which has less and which has more of the quality represented by the variable - Quantitative - can be rank ordered |
Ordinal Variables |
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Can be rank ordered sizes of differences can be quantified and compared between them F and C |
Interval variables |
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Very similar to interval variables BUT have an identifiable absolute zero point x times 2 is more than y |
Ratio variables |
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When the null hypothesis is _________, the outcome is statistically significant. |
rejected |
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When the null hypothesis is _______, the outcome is not statistically significant. |
not rejected |