Chapter 1 Introduction to Statistics
collections of observations
The science of planning studies and experiments, obtaining data, and then organizing, summarizing , presenting, analizing, interpreting, and drawing conclusions based on the data
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
the collection of data from every member of the population
is a subcollection of members selected from the population
The likelyhood of getting these results by chance is very small
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.
a numerical measurement of a population
a numerical measure of a sample
(numerical) consists of numbers representing counts of measurments.
(qualitive or attribute) consists of names or labels that are not numbers representing counts or measurements
results when the number of possible values is either a finite number or a countable number (1,2,3,etc)
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)
Nominal level of measurement
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
Ordinal level of measurement
can be arranged in some order, but differences (obtained by subtraction) between data values either cannot be obtained or are meaningless. Ex. Rank, Grades
Interval level of measurement
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
Ratio level of measurement
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.
Voluntary response Sample
one in which the respondents themselves decide wheather to be included.
observe and measure specific characteristics, but we do not attempt to modify the subject studied.
apply treatment and then observe its effects on the subject.
Simple random sample
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
members from the population are selected in such a way that each individual member in the population has an equal chance at being selected
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)
select some starting point and then select every kth (75th) element.
use results that are very easy to get
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)
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
sectional study - data are observed, measured, and collected at one point in time.
(case-control) data are collected from the past by going back in time. (through examination of records, interviews, etc)
(longitudinal) data are collected in the future from groups sharing common factors (called cohorts
occurs in an experiment when you are not able to distinguish among the effects of different factors.
is the difference between a sample result and the true population result; such an error results from chance sample fluctuation.
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.