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Prob and Stats

Chapter 1 Key Terms

Statistics The science of planning studies and experiemnts, obtaining data, organizing, summerizing, presenting, analyzing, interpreting, and drawing conclusions based on the data.
Data Collections of observations For Example: measurements, genders, survey responses.
Population the complete collection of all individuals to be studied.
Census collection of data from every member of a population.
Sample subcollection of members selected from a population.
Voluntary Response Sample A sample for which the respondents themselves chose to be involved.
1.2 Key Terms 1.2 Key Terms
Context Description of what data represents and where that data came from, and why is was collected
Source Where the data orginated
Sampling method Selection of random or unbiased observations from the whole.
Conclusions A clear statement of the results.
Statistical Significance The outcome does not happen by chance.
Practical Significance Statistically significant and significant enough to take action.
1.3 Key Terms 1.3 Key Terms
Parameter numerical measurements describing some characteristic of a population.
Statistic numerical measurements describing some characteristic of a sample.
Quantitaive data numbers represeting counts/measurements.
Categorical data names or labels that are not numbers represeting counts of measurements.
Discrete data the number of possible values is finite.
Continuous Data The number of possible values is infinte with no gaps, interruptions, or jumps.
Nominal Characterized by data that consists of names, labels, or categories only. The data cannot be arranged in an ordering scheme (such as high-low)
Ordinal Data that can be arranged in some order, but difference (obatained by subtraction) between data values either cannot be determined, or are meaningless.
Interval Like ordinal, with the additional poperty that the difference between any two data values is meaningful. However, data at this level does NOT have a NATURAL zero starting point.
Ratio Like interval, however there is a natural zero starting point. Difference and ratios are both meaningful.
1.4 Key Terms 1.4 Key Terms
Bad Graphs Depictions of shape of graph is misleading.
Correlation Causality Two variables may seem lined, smoking and pulse rate, this relationship is called correlation. Cannot conclude one causes the other. CORRELATION DOES NOT IMPLY CAUSALITY.
Small Samples Conclusions should not be based on samples that are far to small. For Example: basing a school suspension rate on 3 students.
Misuses of Percentages Cannot decrease by more than 100%. Once something is decreased by 100%, there is nothing left.
Loaded Questions Intentionally worded to elicit a desired result. "Too little money is being spent on welfare" VS "Too little money is being spent on assistance to the poor." Results 19% VS 63%
Order of Questions Questions are unintentionally loaded by such factors as the order of the items being considered.
NonResponse Occurs when someone either refuses to respond to a survey or is unavailable.
Missing Data Subjects may drop out for reasons unrelated to the study. Low income people are less likely to report income.
Self-Interest Studies Some parties with interest to promot will sponsor studies. When assessing validity, always consider whether the sponsor might influence the results.
1.5 Key Terms 1.5 Key Terms
Observation Studies Observing and measuring specific characteristies without attempting to modify the subect being studied.
Experimental Studies Appl some treatment then observe its effects on the subjects.
Experimental Units Subjects in experiments.
Types of Sampling: Random Selection so that each individual member has equal chance of being selected.
Simple Random Of N subjects selected in suach a way that every possible sample of the same size N has the same chance of being chosedn.
Systematic Select some starting point and then select every Kth element in the population.
Conveniance Use reults that are east to get.
Stratified Subdivide the population into at least 2 different groups that share the same characteristic, then draw a sample from each subgroup (or Stratum)
Cluster Divide the population are into sections (or Clusters), radomly select some of these clusters, choose all members from selected clusters.
Multi-stage Collect daya by using some combination of teh basic sampling methods. Pollsters selsct a sample in different stages, and each stage may use different sampling methods.
Types of Studies: Cross-Sectional Data are observed, measure, and collected at one point in time.
Retrospective (Case Control) Data are collected from the past by going back in time (examine records, interviews...)
Prospective (Longitudinal or corhort) Data are collected int he future from groups sharing common factorys (Cohorts)
Techniques which improve Experimental Design: Randomization Used when subjects are assigned to different groups though a process of random select. The logic is to use chance as a way to create 2 groups that are similar.
Blinding A technique in which the subject doesn't know whether he/she is recieving a treatment or placebo.
Double Blinding The subject and the experimenter do not know whether the treatment is or is not a placebo.
Replication The repition of an experiemnt on more than one subject. Use large enough samples so that the erratic behavior that is characteristic of very small samples will not disguise the true effects of different samples.
Avoid Confounding Confoundin occurs in experiments when the experimenter is not able to distinguish between the effects of different factors. Try to plan an experiment so that this does not occur.
Created by: winterholerrh