EBP: Class 3 Part 2

typically you have to infer the null hypothesis from the purpose statement

to test whether the results achieve "statistical significance". A statistically significant result is one that is very unlikely to be due to chance variations or sampling error.

• conclude that the difference b/w the test groups is large enough that it is unlikely to be due to chance alone. Reject the null hypothesis and conclude that the groups really do differ.
• Conclude that the difference b/w the groups could be explained just by chance. Fail to reject the null hypothesis, at least for now

you reject the null hypothesis when the outcome of null hypothesis is actually true

you do not reject the null hypothesis but the outcome of the null hypothesis is actually false

use our data to calculate the probability that our finding is just due to chance, under the null hypothesis. This is called the p-value. If p-value is small enough, we reject the null hypothesis and conclude there is a difference

• alpha
• convention is that alpha=0.05, or one Type I error in every 20 experiments/studies

the probability of a difference occuring purely by chance

"Given the data we have, there is a 5% chance that there really is no difference."

the extent to which a researcher is willing to be wrong is the alpha level

• researcher sets the significance level, also called alpha, at the outset of study
• value of alph determines how difficult it will be for the researchers to claim that their results are statistically significant
• alph level is expressed as a probability, most commonly p<.05
• --there is probability of less than 5-in-100 that the difference between groups is due to sampling error

if the difference b/w groups is unlikely to be due to chance/random error

• reject the null hypothesis
• --conclude there is a difference b/w the groups

I reject my null hypothesis: I conclude there is a difference b/w groups

I fail to reject my null hypothesis: I conclude that there is no difference b/w groups

• sample size
• difference in means
• variation of your measurements
• alpha level you require for the p-values

statement that population parameter will meet some test of difference for some specified probability for any sample

statement that population parameter will fall within interval for some specified probability (confidence level) for any sample

a probability that the population parameter falls somewhere w/in the interval

the unknown population parameter, the estimated range being calculated from a given set of sample data

• upper limit
• lower limit

how uncertain we are about the unkown population parameter.

more data should be collected before anything very definite can be said about the parameter

they provide a range of plausible values for the unknown parameter

• number of samples/groups compared
• independence or dependence of sample data
• level of data (nominal, ordinal, ratio, etc.)
• other assumptions met for parametric statistics

• samples that have no effect on each other
• two samples: unparied t-test
• more than two samples: anaylysis of variance (ANOVA)

• matched pairs
• one group tested more than once
• two samples: paired t-test
• more than two samples: repeated measures analysis of variance

• the test you conduct on data is determined by number of groups and kind of data you're analyzing
• the study data are plugged into a formula, and the "value" of the statistic is computed
• this value is then evaluated to see if it is likely or unlikely to be due to error

• specifies which of the group means the researcher expects to be greater than the other(s)
• is justified only when evidence exists to support the expectation
• testing for a difference that goes in one direction

• specifies only that the group means will differ, not which one is expected to be greater than the other
• appropriate when existing evidence does not support the superiority of one method over the other(s)
• researcher can test for differences that go in either direction (two tails)
• probability of creating a Type I error needs to be split between the 2 directions

one-tailed test, w/ .05 all in one direction, makes it easier to reject the null hypothesis than a two-tailed test, which has to reach a .025 probability level at one of the ends for a difference to be statistically significant. If a researcher specifies a directional hypothesis and uses a one-tailed test, but the data turn out to be in the direction opposite to that expected, the researcher cannot reject the null hypothesis

examines relationships between variables as opposed to comparison (how alike measures of variables are)

quantify the strength and direction of association between two variables

0.30-0.45

.5-100

scatter plots

direct association between 2 variables. As one variable becomes larger, the other also becomes large, and vice versa

as the value of one variable increases the associated variable decreases. As one variable becomes large, the other gets smaller, and vice versa

• used for prediction
• simple linear regression
• multivariate regression

used to estimate population parameters

assumptions about the data

• Sample randomly drawn from population has a normal distribution
• variances of samples being compared are roughly equal
• data are interval or ratio scale--therefore data can be subjected to arithmetic manipulations to calculate means and standard deviations

researchers must use nonparametric statistics

• when we calculate p-value, we find probability that the sample was different due to sampling variability
• try to see if recorded value occurred by chance and chance alone

• test done w/out assumption of normality, approximate normality, or symmetry
• test don't require mean and standard deviation. Mean can be easily influenced by outliers or skewness, and we aren't assuming normality, a mean no longer makes sense
• one deals w/ median rather than mean. Median judges location, makes more sense
• used w/ small samples, and w/ nominal and ordinal data
• assumptions for parametric statistics often can be violated w/out major problems, such as use w/ ordinal data and small samples

• validity of study in regard to your clinical question (population, age, diagnosis)
• variables studied (do they link w/ your question?)
• reliability of measures used in the study
• statistical analysis (parametric? non-parametric?)
• overall strengths and weaknesses of the study
• other issues