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WGu RFC 1 ch 13
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WGu RFC 1 ch 13
Summary of Chapter 13
Question  Answer 

inferential statistics  deal with inferences about populations based on the behavior of samples 
inferential statistics are  used to determine how likely it is that results based on a sample or samples are the same results that would have been obtained for the entire population 
the degree to which the results of a sample can be generalized to a population is  always expressed in terms of probabilities not in terms of proof 
expected, chance variation among means  referred to as sampling error 
The question that guides inferential statistics is  whether observed differences are real or only the result of sampling error 
Useful characteristic of sampling errors  usually normally distributed 
if a sufficiently large number of equal sized large samples are  randomly selected from a population, the means of those samples will be normally distributed around the population mean 
the smaller the standard of error of the mean  the less sampling error 
as the size of the sample increases  the standard of error of the mean decreases 
standard error  can also be calculated for other measures of central tendency as well as for measure of variability, relationship, and relative position 
standard error  can also be determined for the difference between the means 
hypothesis testing  process of decision making in which researchers evaluate the results of a study against their original expectations 
hypothesis testing  process of determining whether to reject the null hypothesis (i.e., no meaningful differences only those due to sampling error) in favor of the research hypothesis (i.e., groups are meaningfully different; one treatment is more effective than another) 
test of significance  statistical procedure in which we determine the likelihood (i.e., probability) that results from our sample are just due to chance 
significance  refers to a selected probability level that indicates how much risk we are willing to take if the decision we make is wrong 
the standard preselected probability level used by educational researchers  is usually 5 out of 100 chances that the observed difference occurred by chance 
tests of significance can be  either one tailed or two tailed 
tails  refers to the extreme ends of the bell shaped curve of a sampling distribution 
one tailed test  assumes that a difference can occur only in one direction; the research hypothesis is directional; the researcher should be quite sure that the results can occur only in the predicted direction 
two tailed test  assumes that the results can occur in either direction; the research hypothesis is nondirectional 
one tailed test has one major advantage  it is statistically easier to obtain a significant difference when using a one tailed tests 
type I error  occurs when the null hypothesis is true, but the researcher rejects it, believe incorrectly, that the results from the sample are not simply due to chance. (you think you are pregnant but you are not, false positive 
type II error  occurs when the null hypothesis is false, but the researcher fails to reject it, believing incorrectly, that the results from the sample are simply due to chance (you think you are not pregnant, but you are, false negative) 
parametric tests  more powerful and appropriate when the variable measured is normally distributed in the population and the data represent an interval or ratio scale of measurement 
non parametric tests  makes no assumptions about the shape of the distribution and are used when the data represent an ordinal or nominal scale 
t test  used to determine whether two groups of scores are significantly different at a selected probability level 
basic strategy of a t test  compare the actual difference between the means of the groups, with the difference expected by the chance if the null hypothesis (i.e., no difference) is true. This ratio is known as the t value 
t value  is equal or greater than the value statistically established for the predetermined significance level, we can reject the null hypothesis 
Simple or one way analysis of variance (ANOVA)  used to determine whether scores from two or more groups are significantly different at selected probability level 
ANOVA  total variance of scores is attributed to two sources variance between groups (variance caused by the treatment or other independent variables) and variance within groups (error variance) 
Analysis of covariance (ANCOVA)  form of ANOVA used for controlling extraneous variables 
ANCOVA  adjusts posttest scores for initial differences on some variable and compares adjusted scores 
ANCOVA  used as a means of increasing the power of a statistical test 
power  refers to the ability of a significance test to identify a true research finding, allowing the experimenter to reject a null hypothesis that is false 
multiple regression  combines variables that are known individually to predict the criterion into a multiple regression equation 
chi square  is a nonparametric test of significance appropriate when the data are in form of frequency counts or percentages and proportions that can be converted to frequencies in different categories or groups 
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Xyrarose