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Biostatistics Exam 2|83 complete Q’s and A’s
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Biostatistics Exam 2|83 complete Q’s and A’s
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Biostatistics Exam 2|83 complete Q’s andA’s
Inferential Statistics - - method that uses samples and probability to reach
conclusions about unmeasurable populations
- answers are given in terms of probabilities because of uncertainty
associated with sampling
- can use known distributions to look at probability that differences between
samples are due to chance
-draw conclusions about experiments based on the probability of getting a
test statistic of a particular magnitude
- Sampling distribution - - Theoretical distribution of a statistic based on all
possible random samples drawn from the same population
- biased sample - - A sample obtained in such a way that certain groups of
the population are systematically underselected or overselected
- null hypothesis is false - - if the difference measured between groups is
highly unlikely to occur by chance
- A distribution that is based on mathematical formulas and logic rather than
on empirical observations. - - theoretical distribution
- normal distribution - - -statistical tests for continuous data
- bell shaped, theoretical distribution that predicts the frequency of
occurrence of chance events
-actual distribution depends on mean and standard deviation
-mean, median, mode are all the same
- If normally distributed data are transformed to z scores, the mean of the
transformed data will be ____ and the standard deviation will be ____. - - 0,1
- A normal distribution in which the scores have been transformed to z-
scores is called a .... - - Standard normal distribution
- Although in theory the normal curve ends, it is convenient to think of the
curve at extending from... - - -3 to +3 standard deviations
- z-score - - a measure of how many standard deviations you are away from
the average or mean, useful to interpret score
, - central limit theorem - - for any population (regardless of score
distribution), the sampling distribution of the mean approaches a normal
distribution as N increases
- independent variable - - explanatory variable
- dependent variable - - response variable
- what makes for a good experiment? - - -random sampling (equal chance of
being drawn, picking one doesn't affect picking another)
-high sample size
- confounding variables - - associated with variable to be tested but not
accounted for
- null hypothesis - - specific statement about a population parameter
-statement that would be interesting to reject
-often identifies the null expectation if the process of interest is not having
an effect
-can also be a specific expectation from a theory
- alternative hypothesis - - includes all other feasible values for the
population parameter besides the value stated in null hypothesis
- cons of normal distribution - - using the normal distribution assumes that
you know population parameters like the parametric standard error of the
mean
- null distribution - - the sampling distribution of outcomes for a test
statistic under the assumption that the null hypothesis is true
-gives us the frequency distribution for our test statistic when the null
hypothesis is true
-can use to calculate the probability of getting a particular value for our test
statistic under null hypothesis
- t distribution - - -W.S. Gossett
-the simplest statistical test involving numerical data: examine whether a
sample came from a specified population or whether two samples differ
(means)
-can use to test null hypothesis
-series of curves that differ in form depending on degrees of freedom
- degrees of freedom - - number of scores that are free to vary for the
statistic tested
-number of observations minus the number of parameters estimated from
sample statistics
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