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Publicada porJosé Luis Silva Martín Modificado hace 9 años
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1 Applied biostatistics Francisco Javier Barón López Dpto. Medicina Preventiva Universidad de Málaga – España baron@uma.es
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2 Statistical inference Talking about the population, knowing just samples. Usually high probability of being right (95%) or low of being wrong (5%) Usually: Confidence interval (C.I. 95%) Statistical test (p<0,05)
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3 For both ways we need: Standard Error Easy to interpret (believe me): We hope the estimate (sample) being near the real value (population). How close? There is a 95% probability of the estimate being not far away than 2 standard errors of the population parameter.
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4 Example Simulation: Let’s get samples from different size of the population and see what happens when we try to estimate the population mean, using the sample mean. Starting with sample size n=4…
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5 Aplic. de la normal: Estimación en muestras The distribution of sample means are almost “normal” It is not as dispersed as the population. It’s S.D it is called standard error (s.e.)
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6 Aplic. de la normal: Estimación en muestras If n grows: The sample mean distribution is more gaussian. Standard error decreases.
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7 Aplic. de la normal: Estimación en muestras Puedo ‘garantizar’ medias muestrales tan cercanas como quiera a la verdadera media, sin más que tomar ‘n bastante grande’ Se utiliza esta propiedad para dimensionar el tamaño de una muestra antes de empezar una investigación.
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8 Sample sizeMeanStd. Error answer 10 women77 6No evidence against 100 women71 1.6No 1000 women73 0.5No Mean value of BUA in young women is 85. ¿women extracted from sample are similar? Use 95% confidence
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9 Simulation Let’s take samples of size 4. 7075 Do smokers weight more than non smokers?
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10 Let’s take samples of size n=30 7075
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11 Let’s take amples of size 4. Two types of error are possible 7075 No differences Accept differences type II error Type I error
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12 , p, statistical significance sstatistical significance = p <
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