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| Question | Answer |
| statistics | the method for collecting, presenting, and analyzing data |
| Descriptive Statistics | Deals with the organization and summary of data. Purpose is to replace what may be and extremely large set of numbers in some dataset with a smaller number of summary measures. |
| Inferential Statistics | Inferential statistics, descriptive statistics is linked with probability theory so that an investigator can generalize the results of a a study of a few individuals to some larger group. |
| Statistical Population | is the total set of elements (objects, persons, regions, neighbourhoods, rivers, etc...) under examination in a particular study. |
| Population Characteristic | is any measurable attribute of an element in the population |
| Variable | is a population characteristic that takes on different values for the elements comprising the population |
| Population Census | is a complete tabulation of the relevant population characteristic for all elements in the population |
| Sample | is a subset of the elements in the population and is used to make inferences about certain characteristics of the population as a whole |
| Sampling Error | is the difference between the value of a population characteristic and the value of that characteristic inferred from a sample |
| Non-Sampling Error | errors that arise in the acquisition, recording and editing of statistical data are termed nonsampling or data acquisition errors |
| Representative Sample | is one in which the characteristics of the sample closely match the characteristics of the population as a whole |
| Random Sample | is one in which every individual in the population has the same chance, or probability, of being included in the sample. (ensures unbiased findings). |
| Statistical Estimation | is the use of the information in a sample to estimate the value of an unknown population characteristic (a best guess of the value of a population characteristic) |
| Hypothesis Testing | is a procedure of statistical inference in which we decide whether the data in a sample support a hypothesis that defines the value (or a range of values), of a certain population characteristic. |
| 3 Paradigms of Geography | exploration, environmental determinism and possibilism, and regional Geography |
| Environmental Determinists and Possibilists | focus on the role of the physical environment as a controlling variable in explaining the diversity of the human impact on the landscape |
| Integration and Synthesis | of the characteristics of areas or regions (Regional Geography) |
| Methods of Data Collection: 2 Approaches | Primary and Secondary |
| Primary Data Collection | first hand sources, researcher collects the data themselves |
| Secondary Data Collection | -Second-hand Sources -Researcher extracts data/information from existing research reports |
| Collecting Primary Data: 2 Main Methods | 1) Experimental – Natural Sciences 2) Non-experimental – Social Sciences Observation*, Questionnaire/Survey, Interview Method selection depends on: Question asked, resources available, researcher skill set, researcher should know as much as possible about the nature of the study population |
| Observation | Purposeful, systematic and selective way of observing an interaction or phenomena as it takes place |
| 2 Methods of Observation | 1) Participant observation – become part of study population 2) Non-participant observation – do not get involved in study population activities |
| Secondary Sources of Data Collection | Possible Sources: Government or private organization reports/publications Earlier research studies (journals/theses) Personal records Mass media Disadvantages: Validity and reliability – varies with source Personal Bias – personal records and mass media Availability of data – purpose it was collected for Format |
| Measurement Evaluation | -Need to understand quality of data that is collected Data can be evaluated on the basis of: Validity Accuracy Precision |
| Validity | Degree of correspondence between the concept being addressed and the variable being used to measure that concept E.g., Air Quality – concentration of lung irritants, # of smog days Important to clearly define concept and select variable (indicator) that is strongly associated with that concept |
| Validity con't | Information being collected is appropriate and adequate. Was the right indicator selected? Data can be valid without being accurate or precise, can be accurate without being valid or precise, can be precise without being valid or accurate. Must evaluate all three. |
| Accuracy | Absence of error - degree of agreement between a measured value and true value. Two components to error: Total error = Systematic error + Random error Systematic – error due to measurement instrument Random – error due to inherent variation in variable |
| Precision | Degree to which repeated measurements show the same results. Related to random component of error. |
| Sampling Terminology | Study Population- Group of study includes all individuals in group Size of population denoted by ‘N’ Sample- Subset group of study population from whom data is actually collected -Number of individuals sampled denoted by ‘n’ Sampling Design- Method for selecting individuals in a sample |
| Sampling Terminology con't | Sampling Unit- The unit on which data is collected (e.g., person, family, plant, river) Recall – established by research question Sample Statistic- Findings based on the information collected from sample (e.g., average income, maximum diversity, minimum age) Estimate of study population characteristics |
| Sampling Terminology con't | Sampling Error- a difference between the sample statistic and the actual value for the study population *Always some error associated with sampling --> Estimate *Want to make error as small as possible *Problem – don’t usually know actual value so difficult to determine error |
| Minimizing Sampling Error | -sample size (n) -variation in study variable -saturation point |
| Sample Size | Larger 'n', the more accurate (less error), the estimate of the actual population value |
| Variation in Study Variable | *Larger variation in variable the greater the sampling error *Corollary – if variable varies greatly in a study population need to have a larger 'n' |
| Saturation Point | *Number of individuals at which further sampling results in no further or more accurate information *Somewhat subjective *more sampling can be redundant if you are already close to the end value |
| Selecting a Sample | Maximize accuracy and precision in your estimates. Avoid bias in selection of sample. Ensure sample is representative of study population. |
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