front 1 Data | back 1 collections of observations |
front 2 Statistics | back 2 The science of planning studies and experiments, obtaining data, and then organizing, summarizing , presenting, analizing, interpreting, and drawing conclusions based on the data |
front 3 Population | back 3 the complete collection of all individuals (scores, people, etc ...) to be studied. The collection is complete in the sense that it includes all the indidviuals to be studied |
front 4 Census | back 4 the collection of data from every member of the population |
front 5 Sample | back 5 is a subcollection of members selected from the population |
front 6 Statistically significant | back 6 The likelyhood of getting these results by chance is very small |
front 7 Practical significant | back 7 The teatment or finding might be statistically significant but common sense might suggest that the finding or treatment does not make enough of a difference to justify its use to be practical. |
front 8 Parameter | back 8 a numerical measurement of a population |
front 9 Statistic | back 9 a numerical measure of a sample |
front 10 Quantitive data | back 10 (numerical) consists of numbers representing counts of measurments. |
front 11 Categorical data | back 11 (qualitive or attribute) consists of names or labels that are not numbers representing counts or measurements |
front 12 Discrete Data | back 12 results when the number of possible values is either a finite number or a countable number (1,2,3,etc) |
front 13 Continuous Data | back 13 results from infinitely many possible values that corrospond to some continuous scale that covers a range of values without gaps, interruptions or jumps. (1.67 liters, 7.437 pounds) |
front 14 Nominal level of measurement | back 14 is data that consists of names, labels or categores only. The data cannot be arranged in an ordering scheme (such as low to high) Ex. political party |
front 15 Ordinal level of measurement | back 15 can be arranged in some order, but differences (obtained by subtraction) between data values either cannot be obtained or are meaningless. Ex. Rank, Grades |
front 16 Interval level of measurement | back 16 is like the ordinal level, with the additional property that the differences between any two values is meaningful. However, data at this level do not have a natural starting point. Ex. Temperature, years |
front 17 Ratio level of measurement | back 17 is the interval level with additional property that there is also a natural zero starting point (where sero indicates that none of the quantity is present). For values at this level, differences and ratios are both meaningful. |
front 18 Voluntary response Sample | back 18 one in which the respondents themselves decide wheather to be included. |
front 19 Observational Study | back 19 observe and measure specific characteristics, but we do not attempt to modify the subject studied. |
front 20 Experiment | back 20 apply treatment and then observe its effects on the subject. |
front 21 Simple random sample | back 21 of n subjects is selected in such a way that every possible sample of the same size n has the same chance of being chosen |
front 22 Random sample | back 22 members from the population are selected in such a way that each individual member in the population has an equal chance at being selected |
front 23 Probability Sample | back 23 involves selectin members from a population in such a way that each member of the population has a known (but not necessarily the same) chance of being selected) |
front 24 Systemic sampling | back 24 select some starting point and then select every kth (75th) element. |
front 25 Convenience sampling | back 25 use results that are very easy to get |
front 26 Stratified sampling | back 26 subdivide the population into at leasst two different subgroups (or strata) so that subjects within the same sungroup share the same characteristics (such as gender or age), then we draw a sample from each sungroup (or stratum) |
front 27 Cluster sampling | back 27 first divide the population into sections 9 or clusters), then randomly select some of those clusters, and then choose all the members from those selected |
front 28 Cross | back 28 sectional study - data are observed, measured, and collected at one point in time. |
front 29 Retrospective study | back 29 (case-control) data are collected from the past by going back in time. (through examination of records, interviews, etc) |
front 30 Prosepctive study | back 30 (longitudinal) data are collected in the future from groups sharing common factors (called cohorts |
front 31 Confounding | back 31 occurs in an experiment when you are not able to distinguish among the effects of different factors. |
front 32 Sampling error | back 32 is the difference between a sample result and the true population result; such an error results from chance sample fluctuation. |
front 33 Nonsampling error | back 33 occurs when the sample data are incorrectly collected, recorded, or analiyzed (such as by selecting a biased sample, using a defective measurment instrument, of copy the data incorrectly. |