PCM1: Measurement & Biostatistics

=Objectives=

Describe the elements that characterize the sample Frequency Distribution (the distribution of observed measures across individuals in a sample or population).

 * 1) Shape (appearance): normal, assymetric, bimodal, etc
 * 2) Measures of Central Tendency (mean, median, mode): extent to which values cluster around a central position
 * 3) Mean is far more effected by outliers, especially with assymetric distributions
 * 4) Measures of Spread (range, variance, standard deviation):

Describe briefly the uses of the sample Frequency Distribution to:

 * Represent the Frequency Distribution in the Population
 * use mean, standard deviations (ie +2 Stdevs from mean), to describe individual datapoints
 * within sample variation estimates the within population variation
 * type of distribution (asymmetric, normal, bimodal,)
 * 1) Represent the set of all possible (hypothetical) samples that could have been drawn from the population.
 * use range, variance, etc.

==Describe the elements that characterize the distribution of Expected Sample Means (also called the Expected Distribution of Estimates of the Mean) that the rules of statistics allow you to construct from the sample Frequency Distribution== a) Shape: often normal b) Measure of Central Tendency: Sample Mean. used to represent hypothetical variation across all possible samples. (mean & standard deviation, standard error of the mean) c) Measure of Spread: Standard Error of the Mean

Interpret the meaning of a Confidence Interval around an estimate of a mean or proportion.

 * Our best estimate for the true mean is observed sample mean
 * The confidence interval gives a likely range for the true population mean.

Describe the Null Hypothesis and the corresponding Null Value for differences and ratios.
Null hypothesis

Define and interpret the p-value used in hypothesis testing.

 * P-value
 * P value is associated with a test statistic. It is "the probability, if the test statistic really were distributed as it would be under the null hypothesis, of observing a test statistic [as extreme as, or more extreme than] the one actually observed."

The smaller the P value, the more strongly the test rejects the null hypothesis, that is, the hypothesis being tested.

A p-value of .05 or less rejects the null hypothesis "at the 5% level" that is, the statistical assumptions used imply that only 5% of the time would the supposed statistical process produce a finding this extreme if the null hypothesis were true.

5% and 10% are common significance levels to which p-values are compared.

Define Power and the two primary factors that influence it.

 * Depends on sample size
 * the effect size that you think is important
 * the level of significance