Question | Answer |

Discrete Data | the number of possible values is either finite number or a countable number # of eggs laid by a chicken |

Continuous (numerical)data | result from infinitely many possible values that correspond to some continuous scale that covers a range of values without gaps, or interuptions. amount of milk from a cow |

Quantative data | representing counts or measurements. weights of super models |

Qualatative (categorical or attribute) data | different categories distinguished into different categories by non numeric characteristic. gender of professional atheletes |

Parameter | Numerical measurement describing some characteristic of a population. Count entire population the number of redlights working and not in a city |

Statistic | a numerical measurement describing some chacteristic of a sample 60% of 800 bell employees have 401K |

Census | Collection of data from every element in a population |

Sample | Subset of a population |

Statistics | Statistics a collection of methods for planning studies and experiments, obtaining data, and then organizing, summarizing, presenting, analyzing, interpreting, and drawing conclusions based on the data |

Data | observations (such as measurements, genders, survey responses) that have been collected |

Population | the complete collection of all elements (scores, people, measurements, and so on) to be studied; the collection is complete in the sense that it includes all subjects to be studied |

Census | Collection of data from every member of a population |

The subject of statistics | is largely about using sample data to make inferences (or generalizations) about an entire population. It is essential to know and understand the definitions that follow. |

Parameter | a numerical measurement describing some characteristic of a population. |

Statistic | a numerical measurement describing some characteristic of a sample. |

Quantitative data | numbers representing counts or measurements. Example: The weights of supermodels |

Qualitative (or categorical or attribute) data | can be separated into different categories that are distinguished by some nonnumeric characteristic Example: The genders (male/female) of professional athletes |

Quantitative data | can further be described by distinguishing between discrete and continuous types. |

Discrete Data | result when the number of possible values is either a finite number or a ‘countable’ number (i.e. the number of possible values is 0, 1, 2, 3, . . .) Example: The number of eggs that a hen lays |

Sample | Subcollection of members selected from a population |

Nomial Level measurement | characterized by data that consit of names labels no ordering scheme Ex. yes, no, undecided |

Ordinal level of measurement | Data that can be arranged by order but differences indata values can not be determined Ex: grades A<B<C<D<F |

Interval level of measurement | like ordinal but diffence in values is meanighfyl no natura zero Ex years 1000,2000,1776,1492 |

Ratio Level of measurement | the interval level with the addtioional proerty there is a natural zero where zero none present Ex textbooks ($0 represents free) |

Nominal | categories only |

Ordinal | categories with some order |

Interval | differences but no natural starting point |

Ratio | differences and a natural starting point |

Voluntary Response Sample VRS | one in which respondents themselves decide whether to be included Ex: mail in, internet poll |

Small Samples Size | Conclusion should not be made on small samole size EX: Suspension rate based only on three students |

Misuse of percentages | 100% is 100% no such thing as 110% |

Observartional Study | observing and measuring specific characteristics withour attemping to midify the subjects being studied. |

Experiment | apply some treatment and observe its effects on the subjects; |

Experimental units | subjects in experiment |

Cross sectional study | data are observed, measued, and collected at one point in time |

Retrisoectuve (case Control) study | data are collected from the past by going back in time. |

Prospective (longitudinal or cohort) study | data are collected in the future from groups (called cohorst )sharing common factors. |

Confounding | occurs in an experiment when the experimenter is not able to distinguish between the effects of different factors. |

Blinding | subject does not know if he is receiving a treatment or a placebo. |

Blocks | groups of subjects with similar characteristics |

Completely Randomized Experimental Design | subjecrts are put into blocks thrught a process of random selection. |

Rigorously Controlled Design | Subjects are very carefully chosen. |

Replication | Repetition of an experiment when there are enough subjects to recognize the differences from different treatments. |

Sample Size | use a sample size that is large enought to see the true nature of any effects andsample using appropriate method such as randomeness |

Random Sample | members of the population are selected in such a way that each individual member has an equal chance of being selected |

Simple Random Sample (of size n) | Subjects selected in such a way that every possible sample of the sme size n has the same chance of being chosen |

Systematic Sampling | Starting point and select every kth element Ex: start at 14 and chose every 5th member after. |

Convience Sampling | use results that are easy to obtain |

Stratified Sampling | subdivide the population into at least two different subgroups that share same characteristics, then draw a sample from each subgroup (or stratum). EX men women groups |

Cluster Sampling | divide the population into sections (clusters); randomly select some of those clusters; choose all members from selectd clusters. Ex choose 3 of 20 precints and interview every body in those precints. |

Methods of sampling | Random Systematic Convenience Stratified Cluster |

Sampling error | The difference between a smple result and the true population result; error results from chance sample fluctuations |

Nonsampling error | Sample data incorrectly collectd, recorded, or analyzed Ex: (biased sample, defectve instrument, copying data incorrectly) |