Statistics

Statistics

1. Data Sets

   • Consists of information coming from observations, counts, measurements, or responses. 
   • Population
     • The collection of all outcomes, responses, measurements, or counts that are of interest.
     • Parameter
       • is a numerical description of a population characteristic.
   • Sample
     • is a subset, or part, of a population.
     • Statistic
       • a numerical description of a sample. 
   • Data Qualifications
     • Qualitative Data
       • Consists of attributes, labels, or nonnumerical entries.
     • Quantitative Data
       • Consists of numerical measurements and counts.
     • Nominal Level of neasurements
       • Are qualitative Data only. Data at this level can be arranged in order, or ranked, but difference between data entries are not meaningful.
     • Ordinal Level of Measurement
       • Are qualitative or quantitative. Data at this level can be arranged in order, or ranked, but differences between data entries are not meaningful.
     • Interval Level of Measurements
       • Can be ordered, and meaningful differences between data entries can be calculated. At the interval level, a zero entry simply represents a position on a scale; the entry is not inherent zero. 
     • Ratio Level of measurement
       • Are similar to data at the interval level, the added property that a zero entry is an adherent zero. A ratio of two data values can be formed so that one data value can be meaningfully expressed as a multiple of another. 

2. Descriptive Statistics

   • The branch of statistics that involves the organization, summarization, and display of data. 

3. Inferential

   • The branch of statistics that involves using a sample to draw conclusions about a population. A basic tool in the study of inferential statistics is probability. 

4. Data Collection

   • Observational Study
     • A researcher observes and measures and characteristics of interest of part of a population but does not change existing conditions. 
   • Experiment
     • A treatment is applied to part of a population and responses are observed. 
     • Control Group
       • Part of the population in which no treatment is applied.  
     • Placebo
       • A harmless unmedicated treatment. 
   • Simulation
     • Is the use of a mathematical or physical model to reproduce the conditions of a situation process. 
   • Survey
     • An investigation of one or more characteristics of a population. 

5. Experimental Design

   • Confounding Variable
     • Occurs when an experimenter cannot tell the difference between the effects of different factors on a variable. 
   • Placebo Effect
     • When the subject reacts favorably to a placebo when in fact the subject has been given no medicated treatment at all. 
   • Blinding
     • A technique where the subjects do not know whether they are a treatment or a placebo.
     • Double-Blind Experiment
       • Neither the experiment nor the subject know if the subjects are receiving a treatment or a placebo. 
   • Randomization
     • A process of randomly assigning subjects to different treatment groups. 
     • Completely Randomized Design
       • Subjects are assigned to different treatment groups through random selection. 
       • Blocks
         • Groups of subjects with similar characteristics.
     • Randomized Block Design
       • The division of subjects with similar characteristics into blocks, and then, within each block, randomly assign subjects to treatment groups. 
   • Matched Pairs Design
     • Where subjects are paired up according to a similarity. 
   • Sample Size
     • The number of subjects in an experimentsl design. 
   • Replication
     • The repetition of an experiment under the same or similar conditions. 

6. Sampling Techniiques

   • Census
     • The count or measure of an entire population.
   • Sampling error
     • The difference between the results of a sample and those of a population. 
   • Random Sample
     • A sample in which every member of the population has an equal chance of being selected. 
     • Simple Random Sample
       • A sample in which every possible sample of the same size has the same chance of being selected. 
   • Stratified Sample
     • A sample that is drawn from a number of separate strata of the population, rather than at random from the whole population, in order that it should be representative.
   • Cluster Sample
     • When the population falls into naturally occurring subgroups. You divide the population into groups called clusters and select the members in one or more clusters (but not all).
   • Systematic Sample
     • A systematic sample is a sample in which each member of the population is assigned a number.
   • Convenience Sample
     • Consists of only available members of a population.