Stats Ch 1 Terms

  1. Statistics
    the study of how to collect, organize, analyze, and interpret numerical information from data
  2. Individuals
    the people or objects included in the study
  3. Variable
    a characteristic of the individual to be measured or observed
  4. quantitive variable
    has a value or numerical measurement for which operations such as addition or averaging make sense
  5. qualitative variable
    describes and individual by placing the individual into a category or group, such as male or female
  6. population data
    the data are from every individual of interest.
  7. sample data
    the data are from only some of the individuals of interest
  8. population parameter
    • a numerical measure that describes and aspect of a population. 
    • Eg. The proportion of males in the population of al climbers who have conquered Mt. Everest
  9. sample statistic
    • a numerical measure that describes an aspect of a sample
    • this is an example of a statistic
    • sample statistics can vary from sample to sample
  10. levels of measurement
    • another way to classify data other than qual or quant
    • levels indicate the appropriate type of arithamatic
  11. nominal level
    • data that consist of names, labels, or categories. 
    • no implied criteria by which data can me ordered from smallest to largest
  12. ordinal level
    applies to data that can be arranged in order. However differences  between data values either cannot be determined or are meaningless
  13. interval level
    applies to data that can be arranged in order. in addition, differences between data values are meaningful.
  14. ratio level
    • applies to data that can be arranged in order. In addition, both differences between data values and ratios of data values are meaningful.
    • Data at the ratio level have a true zero.
  15. descriptive statistics
    involves methods of organizing, picturing, and summarizing information from samples or populations
  16. Inferential statistics
    involves methods of using information from a sample to draw conclusions regarding the population
  17. simple random sample 
    of n measurements from a population is a subset of the population selected in such a manner that every sample of size n from the population has an equal chance of being selected.
  18. simulation 
    a numerical facsimile or representation of a real-world phenomenon
  19. sampling with replacement
    • in some software this means that although a number is selected fro the sample, it is not removed from the population
    • the same number may be selected for the sample more than once
  20. random sampling
    use a simple random sample from the entire population
  21. stratified sampling
    • divide the entire population into distinct subgroups called strata.
    • The strata are based on a specific characteristic such as age, income, etc.
    • All members of a stratum share the specific characteristic.
    • Draw random samples from each stratum.
  22. systematic sampling
    number all members of the population sequentially. Then, from a starting point selected at random, include every kth member of the population in the sample.
  23. cluster sampling
    divide the entire population into pre-existing segments or clusters. The clusters are often geographic. Make a random selection of clusters. Include every member of each selected cluster in the sample.
  24. Multistage sampling
    Use a variety of sampling methods to create successively smaller groups at each stage. The final sample consists of clusters.
  25. Conveinence sampling
    • Create a sample by using data from population members that are readily available
    • risk of being severely biased
  26. sampling frame
    • a list of individuals from which a sample is actually selected
    • idealy the entire population
  27. undercoverage
    results from omitting population members from tjhe sample frame
  28. sampling error
    • the difference between measurements from a sample and corresponding measurements from the respective population. it is caused by the fact that the sample does not perfectly represent the population
    • these do not represent mistakes
    • simply the consequences of using samples instead of populations
  29. nonsampling error
    the result of poor sample design, sloppy data collection, faulty measuring instruments, bias in questionnaires, and so on
  30. census
    measurements or observations from the entire population are used
  31. sample
    measurements or observations from part of the population are used.
  32. observational study
    observations and measurements of individuals are conducted in a way that doesn't change the response ofr the variable being measured
  33. experiment
    a treatment is deliberately imposed on the individuals in order to observe a possible change in the response or variable being measured
  34. placebo effect
    occurs when a subject receives no treatment but (incorrectly)believes he or she is in fact receiving treatment and responds favorably.
  35. control group
    • the group that receives dummy treatment, enabling researchers to control for the placebo effect
    • used to account for influence or other known or unknown variables that might be an underlying cause of a change in response in the experimental group
  36. treatment group
    they get the real treatment
  37. completely randomized experiment
    one in which a random process is used to assign each individual to one of the treatments.
  38. block
    a group of individual sharing some common features that might affect the treatment
  39. randomized block experiment
    individuals are first sorted into blocks, and then a random process is used to assign each individual in the block to one of the treatments
  40. randomization
    used to assign individuals to the two treatment groups. this helps prevent bias in selecting members for each group
  41. replication
    repeat the experiment on many paitents reduces the possibility that the differences in pain relief for the two groups occurred by chance alone
  42. double -blind
    • neither the individuals or the observers know which subjects are receiving the treatment
    • help control for suibtle biases that a doctor might pass on to a patient
  43. surveying
    a common means to gather data about people by asking them questions
  44. Likert scale
    a scale like strongly disagree to strongly agree
  45. nonresponse
    • individuals either cannot bve contacted or refuse to participate.
    • nonresponse can result in significant undercoverage of a population
  46. voluntary response
    individuals with strong feelings about a subject are more likely than others to respond.
  47. lurking variable
    one for which no data have been collected but that nevertheless has influence on other variables in the study
  48. confounded variable
    • for two variables when the effects of one cannot be distinguished from the effects of the other.
    • they may be part of the study, or they may be outside lurking variables
  49. generalizing results
    to generalize the findings of a study to a situation of wider scope than that of the actual data setting
  50. study sponsor
    a concern for studys as subtle bias may be introduced
Card Set
Stats Ch 1 Terms