# AP Statistics Curriculum 2007 IntroUses

(Difference between revisions)
 Revision as of 18:29, 14 June 2007 (view source)IvoDinov (Talk | contribs)m ← Older edit Revision as of 23:02, 14 June 2007 (view source)IvoDinov (Talk | contribs) Newer edit → Line 11: Line 11: ===Model Validation=== ===Model Validation=== - Checking/affirming underlying assumptions. + * '''Unrepresentative Samples''' - these are collections of data measurement or observations that do not adequately describe the natural process or phenomenon being studied. The phrase ''garbage-in, garbage-out'' refers to this situation and implies that none of the concusions or the inference based on such unrepresentative samples should be trusted. In general, collecting a population representative sample is a hard experimental design problem. + ** Self-selection - voluntary response samples, where the respondents, units or participants decide themselves whether to be included in the sample, survey or experiment. + ** ''Non-sampling errors'' (e.g., non-response bias) are errors in the data collection that are not due to the process of sampling or the study design. + ** ''Sampling errors'' arise from a decision to use a sample rather than entire population. + * '''Samples of small sizes'''. + * '''Loaded Questions''' in surveys or polls. + * '''Misleading Graphs''' - Look at the quantitative information represented in a chart or plot, not at the shape, orientation, relation or pattern repreesnted by the graph. + ** Partial Pictures + ** Deliberate Distortions + ** Scale breaks and axes scaling + * '''Inappropriate estimates or statistics''' - erroneous population parameter estimates (intentionally or most likely unintentionally). The source of the data and the method for parameter estimation should be carefully reviewed to avoid bias and misinterpretation of data, results and to guarantee robust inference. * TBD * TBD

## General Advance-Placement (AP) Statistics Curriculum - Uses and Abuses of Statistics

### Uses and Abuses of Statistics

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### Approach

Models & strategies for solving the problem, data understanding & inference.

• TBD

### Model Validation

• Unrepresentative Samples - these are collections of data measurement or observations that do not adequately describe the natural process or phenomenon being studied. The phrase garbage-in, garbage-out refers to this situation and implies that none of the concusions or the inference based on such unrepresentative samples should be trusted. In general, collecting a population representative sample is a hard experimental design problem.
• Self-selection - voluntary response samples, where the respondents, units or participants decide themselves whether to be included in the sample, survey or experiment.
• Non-sampling errors (e.g., non-response bias) are errors in the data collection that are not due to the process of sampling or the study design.
• Sampling errors arise from a decision to use a sample rather than entire population.
• Samples of small sizes.
• Loaded Questions in surveys or polls.
• Misleading Graphs - Look at the quantitative information represented in a chart or plot, not at the shape, orientation, relation or pattern repreesnted by the graph.
• Partial Pictures
• Deliberate Distortions
• Scale breaks and axes scaling
• Inappropriate estimates or statistics - erroneous population parameter estimates (intentionally or most likely unintentionally). The source of the data and the method for parameter estimation should be carefully reviewed to avoid bias and misinterpretation of data, results and to guarantee robust inference.
• TBD

• TBD

### Examples

Computer simulations and real observed data.

• TBD

### Hands-on activities

Step-by-step practice problems.

• TBD

• TBD