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Applied biostatistics

Applied biostatistics. Francisco Javier Barón López Dpto. Medicina Preventiva Universidad de Málaga – España baron@uma.es. Statistical inference. Talking about the population, knowing just samples. Usually high probability of being right (95%) or low of being wrong (5%) Usually:

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Applied biostatistics

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  1. Applied biostatistics Francisco Javier Barón López Dpto. Medicina Preventiva Universidad de Málaga – España baron@uma.es

  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)

  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.

  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…

  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.)

  6. Aplic. de la normal: Estimación en muestras • If n grows: • The sample mean distribution is more gaussian. • Standard error decreases.

  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.

  8. Mean value of BUA in young women is 85. ¿women extracted from sample are similar? • Use 95% confidence

  9. Simulation Do smokers weight more than non smokers? 70 75 Let’s take samples of size 4.

  10. Let’s take samples of size n=30 70 75

  11. Let’s take amples of size 4. Two types of error are possible type II error Accept differences No differences 70 75 Type I error

  12. 0,05, p, statistical significance • statistical significance = p < 0,05

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