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• W3161820912 abstract "Medical statistics is known to be challenging, and medical doctors tend to have considerable fear about the very word “statistics.” Without mathematical background, complex statistical equations may be very difficult to understand. Moreover, statisticians teach statistics to doctors with unfamiliar words, which are just basic to the experts. The advent of programs like SPSS provides the unique opportunity to use common statistical method without getting too bogged down in equations. Understanding the basic concepts of statistics and learning practical knowledge could make investigators use those programs with confidence. This lecture is designed to provide conceptual understanding in order to reduce the fear and to arouse interest in statistics for true beginners who are not familiar with the statistics. In the testing of statistical hypothesis, the alternative hypothesis and the null hypothesis are the two rival hypotheses. The null hypothesis refers to a general or default position: there is no relationship between two measured phenomena, or a potential medical treatment has no effect. The alternate hypothesis will typically be the expected relationship or expected changes. And then, statistical hypothesis tests define a procedure that controls the probability of incorrectly deciding that null hypothesis (a default position) is incorrect based on how likely it would be for a set of observations to occur if the null hypothesis were true. Rejecting or disproving the null hypothesis is a central task in statistical hypothesis testing. The probability of making an incorrect decision is not the probability that the null hypothesis is true, nor whether any specific alternative hypothesis is true. Before beginning hypothesis testing, the first thing investigator have to do is to classify the different types of data according to their nature. Variables can be broadly classified into categorical or continuous ones. A categorical variable is made up of categories, which names distinct entities such as male or female, children or adults, and smoker or non-smoker. A continuous variable is one that gives us a score for each objective, and can take on any value on the measurement scale that we are using, such as height, weight, and certain scoring systems of disease activity. Because the type of data determines the type of statistical method appropriate for the analysis, knowing the different types of data is essential to start statistical analysis. Statistical hypothesis are based on the statistical theory of confidence intervals (CIs). The commonly used 95% CI refers to a range that you can be 95% sure contains the population value. The hypothesis testing computes a range that you can be 95% sure would contain experimental results if the null hypothesis were true. Any result within this range is considered statistically insignificant, and any result outside this range is considered statistically significant. In this session, we will discuss the basic statistical concepts such as estimation and hypothesis testing, 95% CI, significance and type I error, and power and type II error thoroughly." @default.
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• W3161820912 date "2013-01-01" @default.
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• W3161820912 title "First step to medical statistics" @default.
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