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| Is the natural discrepancy, or the amount of error, between a sample statistic and its corresponding population parameter. |
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| Distribution of sample means |
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| Is the collection of sample means for all the possible random samples of a particular size (n) that can be obtained from a population. |
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| Is a distribution of statistics obtained by selecting all possible samples of a specific size from a population. |
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| Is the mean of the distribution of sample means & is equal to the the mean of the population of scores (μ). |
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| The standard error of M(σm) |
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| Is the standard deviation of the distribution of sample means. This provides a measure of how much distance is expected on average between a sample mean (M) & the population mean (μ). |
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| Is the symbol for standard deviation of a sample. |
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| Is the symbol for standard deviation of a population. |
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| Is the symbol for alternative hypothesis. |
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Is the statement that there is an effect.
It is an inequality, saying that p is different from the number mentioned in H0. (H1 could specify <, >, or just ≠.) |
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| Is the symbol for the null hypothesis. |
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Is the statement that there is no effect.
It is an equation, saying that p, the proportion in the population (which you don’t know), equals some number. |
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| Is the symbol for Probability value |
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| Tells you how likely it is to get the sample you got (or a more extreme sample) if the null hypothesis is true (H0). |
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| Is the symbol for population size. (Normally used for sample size) |
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| Is the symbol for frequency. |
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| The sizes of categories that can be shown as raw counts. |
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| Is the symbol for z-score. |
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| Is a data point, that shows how many standard deviations it lies above or below the mean. |
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| States that the larger the sample size (n), the more probable it is that the sample mean will be close to the population mean. |
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| States that for any population with a mean (μ) and standard deviation (σ), the distribution os sample means for a sample size (n) will have a mean of μ and standard deviation σ/ square root of n & will approach a normal distribution as n approaches infinity. |
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Definition
Cohen's d = mean difference (M-μ)
standard deviation (σ)
&
μ treatment - μ no treatment
σ |
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Definition
| Is not influenced at all by the sample size. |
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Definition
| Is the probability that the test will correctly reject a false null hypothesis. (Is The probability that the test will identify a treatment effect if one really exists.) |
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Definition
| Is intended to provide a measurement of the absolute magnitude of a treatment effect, independent of the size of the sample (s) being used. |
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| Is the measurement of the distance between two means and is typically reported as a positive number even when the formula produces a negative vale. |
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Term
Directional hypothesis test
(one tailed test) |
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Definition
| Is the statistical hypotheses (Ho & H1) specify either an increase or a decrease in the population mean. (It makes a statement about the direction of the effect.) |
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Term
| Statistically significant |
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Definition
| The result is sufficient to reject the null hypothesis. Thus, a treatment has a significant effect if the decision from the hypothesis test to reject Ho. (it is very unlikely to occur when the null hypothesis is true.) |
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Definition
Occurs when a researcher fails to reject a null hypothesis that is really false.
(Occurs when the sample mean is not in the critical region.) |
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Definition
Is used to define the concept of very unlikely in hypothesis test. Determines the probability of obtaining sample data in the critical region even though the null hypothesis is true.
(for a hypothesis test it is the probability that the test will lead to a type 1 error.) |
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Definition
| Occurs when a researcher rejects a null hypothesis that is actually true. (The conclusion that a treatment does have an effect when in fact it has no effect.) |
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| Is composed of the extreme sample values that are very unlikely (defined by Alpha level) to be obtained if the null hypothesis is true. |
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| Is a statistical method that uses sample data to evaluate a hypothesis about a population. |
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| Indicates that the sample data are converted into a single, specific statistic that is used to test the hypotheses (T or Z scores). |
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| Is power; the probability of rejecting the null hypothesis when it is false. |
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Is affected by
Sample size – larger sample, more power.
Alpha level – larger alpha level, more power.
One-tailed vs two-tailed tests – one-tailed test more powerful.
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| Z score for samples formula |
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Mean difference (M-μ)
Sample standard deviation (σ) |
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| Estimated standard error formula |
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