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Lecture 5

Winter Week 5 - Inferential Statistics (Introduction)

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Department
Nursing
Course
NUR 80A/B
Professor
L.Atack
Semester
Fall

Description
Winter Week 5 Inferential Statistics IntroductionMarch1411354 PMReviewDescriptive statistics reviewUsed to organize and describe characteristics of a collection of data Some examples of descriptive statisticsFrequency distributionsMeasures of central tendencyvariabilityContingency tablesInferential statisticsOften the next step after data have been summarized Based on laws of probability and sampling distributions Used to make inferencesfrom a small group sample to a larger group population makes inferences about the population based on the samples characteristicsWe estimate population parameters population values from sample statisticsHow inferential statistics workResearchers collect data from one sample ideally representative of the population probability sampleHowever even with random sampling sample characteristics often are not identical to population ie sampling erroroccurs Therefore researcher must decide whether sample values statistics are a good estimate of population parametersExampleA researcher is interested in knowing the level of knowledge that nursing students have in statistics before they take the research courseThe researcher randomly selects 25 nursing students from a population of 500 in a nursing program and asks them to complete a statistical knowledge testThe mean score on the test is 75 The researcher must then determine the likelihood probabilitythat this mean reflects the actual level of knowledge within the population of nursing studentsInferential statistics help the researcher determine the likelihood probability that study findings reflect the actual population parameter vs just being due to chanceInferential statistics provide objective measuresresearchers can compare study findings with to determine the probability that their findings occurred by chance aloneStatistical inference two formsEstimation of parametersUsed to estimate a single parameter or value eg a population mean as in our example Two forms of estimationPoint estimation a single statistic eg mean score on a statistics testInterval estimation a range of values within which the parameter has a specific probability of lying 95 99 confidence interval CIeg 95 CI for our test mean is 6585 Hypothesis testing more commonProvides objective criteria for deciding whether research hypothesis should be accepted as true or rejected as false Example we hypothesize that maternity patients exposed to a film on breastfeeding would breastfeed longer than mothers who were not If the resultant data is in the expected direction can we conclude that this hypothesis is supported There can be two explanations for the above results 1 the film was effective 2 the difference in this sample was due to chance factors Number 1 is the researchers research hypothesisNumber 2 is the null hypothesisstates that there is no relation between the independent and dependent variablesIt cannot be demonstrated directly that the research hypothesis is correct but it is possible to show using inferential statistics that the null hypothesis has a high probability of being incorrectThe focus of statistical decision making is to either accept the null hypothesis or reject itRejection of the null hypothesis is what researchers seek to accomplish through statistical testsThe decision to accept or reject the null hypothesis is based on statistical significance of the findingsPart 1 SignificanceThe term significant in statistics means study findings are likelydue to some systematic influence and not dueto chance aloneOne of the most useful tools for making decisions about populations based on the information aboutResearch Page 1
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