68 terms

population

complete set of people or things being studied

population parameters

specific numbers describing characteristics of the population

sample

subset of the population from which data are actually obtained

raw data

the actual measurements or observations collected from the sample constitute

sample statistics

numbers describing characteristics of the sample found my consolidating or summarizing the raw data

margin of error

used to describe the range of values, or confidence interval, likely to contain the population parameter

from (sample statistic-margin or error)

to (sample statistic + margin of error)

from (sample statistic-margin or error)

to (sample statistic + margin of error)

census

the collection of data from every member of a population

representative sample

sample in which the relevant characteristics of the sample members are generally the same as the characteristics of the population

bias

if a study is designed to favor certain results

random sample

every member of the population has an equal chance of being selected to be part of the sample

simple random sampling

every possible sample of a particular size has an equal chance of being selected

systematic sampling

using a simple system to choose the sample, such as selecting every 10th or 50th member of the population

convinence sampling

using a sample that happens to be convenient to select

cluster sampling

we first divide the population into groups and select some of these groups at random. we than obtain the sample by choosing all the members within each of the selected clusters

stratified sampling

we use this method when we are concerned about differences among subgroups within a population. we first identify the strata and then draw a random sample within each stratum. The total sample consists of all the samples from the individual strata

observational study

we observe or measure characteristics of the subjects, but do not attempt to influence or modify these treatments

experiment

researchers apply some treatment and observe its effects on the subjects of the experiment

variables of interest

items or quantities that the study seeks to measure

explanatory variable

variable that may explain of cause the effect

response variable

variable that responds to changes in the explanatory variable

retrospective (case- control study)

observational study that uses data from the past to learn about issues of concern

ex. effects of alcohol on children of pregnant mothers

ex. effects of alcohol on children of pregnant mothers

prospective (longitudinal) study

set up to collect data in the future from groups that share common factors

ex. harvard nurses' health study

ex. harvard nurses' health study

treatment group

the group of subjects who receive the treatment being tested

control group

subjects who do not receive the treatment being tested

confounding

when different variables are mixed so we cannot determine the specific effects of the variables of interest

confounding variables

variables that lead to the confusion

placebo effect

when people improve because they believe that they are receiving useful treatment

placebo

something that looks or feels just like the treatment being tested but lacks its active ingredients

experimenter effect

when a researcher or experimenter somehow influences subjects through such factors as facial expression, tone of voice, or attitude

single-blind experiment

when the participants don't know which group they belong to, but the experimenters do

double-blind

neither the participants nor any experimenters know who belongs to the treatment group and who belongs to the control group

meta-anaylsis

researchers review many past studies

qualitative data

consist of values that can be placed into nonnumerical categories

quantitative data

consist of values representing counts or measurements

can be classified as continuous or discrete

can be classified as continuous or discrete

continuous data

data can take on any value in a given interval

discrete

data can take on only particular, distinct values and not other values in between

nominal(names for categories) level of measurement

characterized by data that consist of names, labels or categories only. cannnot be ranked or ordered

ordinal (order) level of measurement

applies to qualitative data that can be arranged in some order (low to high)

interval level of measurement

applies to quantitative data in which intervals are meaningful, but ratios are not. data at this level have an arbitrary zero point

ex. farenheight scale

ex. farenheight scale

ratio level of measurement

applies to quantitative data in which both intervals and ratios are meaningful. data at this level have a true zero point

ex. weight

ex. weight

random error

occurs because of random and inherently unpredictable events in the measurement process

ex. trying to get a moving babies weight

ex. trying to get a moving babies weight

systematic error

occurs when there is a problem in the measurement system that affects all measurements in the same way

absolute error

describes how far a claimed or measured value lies from the true value

relative error

compares the size of the absolute error to the true value

r.e.= a.e. / true value x 100

r.e.= a.e. / true value x 100

accuracy

describes how closely a measurement approximates a true value

precision

describes the amount of detail in a measurement

absolute change

the actual increase or decrease from a reference value to a new value

a.c. = new value- reference value

a.c. = new value- reference value

relative change

the size of the absolute change in comparison to the reference value and can be expressed as a percentage

r.c.= new value- ref value/ ref value x 100%

r.c.= new value- ref value/ ref value x 100%

absolute difference

the difference between the compared value and the reference value

relative difference

describes the size of the absolute difference in comparison to the reference value and be be expressed as a percentage

index numbers

provides a simple way to compare measurements made at different times or in different places

mean

average value

median

middle value

mode

most common value

outlier

value that is much higher or much lower than almost all other values

symmetric, normal, bell-curved

if its left half is a mirror image of its right half

ex. heights of women

ex. heights of women

left-skewed

if its values are more spread out on the left side (tail-end if left)

ex. olympic qualifying long jumps results (meters)

ex. olympic qualifying long jumps results (meters)

right-skewed

if its values are more spread out on the right side (tail end right)

ex. family incomes

ex. family incomes

range

difference between its highest and lowest data values

first quartile (lower q)

divides the lowest fourth of a data set from the upper three fourths. it is the median of the data values in the lower half of the data set

second quartile (middle)

overall median

third q (upper q)

divides the lowest three-fourths of a data set from the upper fourth. it is teh median of the data values in the upper half of a data set

five- number summary

low value, lower quartile, media, upper quartile, high value

nth percentile

divides the bottle n% of data values from the top

standard deviation

quantity calculated to indicate the extent of deviation for a group as a whole

z-score (standard score)

the number of standard deviations a data value lies above or below the mean

positive for data values above the mean and negative for data values below the mean

z= data value- mean

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standard dev.

positive for data values above the mean and negative for data values below the mean

z= data value- mean

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standard dev.

empirical rule

within 1 s.d. of mean = 68

within 2 s.d. of mean= 95

within 3 s.d. of mean= 99.7

within 2 s.d. of mean= 95

within 3 s.d. of mean= 99.7

central limit therom

as sample size increases, the shape of sampling distribution becomes more normal