Welcome to test of hypothesis

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Hello All

This will be the blog to understand statistical hypotheses testing!

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Sample size and measurements accuracy

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Seems obvious that larger sample size will give more close results to what happens in realty. Cool, if it's easy to select large elements to represent the sample, then we should do. Usually a sample of size 30 to 40 elements is great to have. We should increase the sample size whenever we: Can easily handle/select sample items (randomly). Perform the measurement with little effort/time consumption. Notice these are highly dependent on the problem or population nature and situations. Another point to keep is to have accurate measurements for the sample as possible. In conclusion, two important factors should be regarded: Good sample size as large as possible. This guarantees more information diversity is included in our sample. Thus it's more close to describe the whole population facts.  Using accurate measuring tools/equipment when measuring the required property of the sample because these measurements will judge next steps. This requireme...
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Normal Probability Distribution

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Also called Gaussian distribution. OK, many things in this world tends, and should do, to be normally distributed. Any distribution is a representation of how the information or data is distributed. We mainly look for its central tendency ( mean ) and variability ( variance ). That's why the normal distribution is usually written as: N ~ (Mu, Sigma^2) For example: the weight of most adult (who still youth) people will normally be centered around some values. Yes, you right there is a diversity: some are slim and some are obese. We may expect the average weight for people (example: ages 20 to 30) to be between 70 to 74 kg. OK, let's consider it as 72 (this is the mean value). Let x represents the weight of a random person. Thus, Expected Value [x] = mean [x] = Mu = 72 kg If we have a sample, we can compute the variance (sigma^2) to indicate variability. But we may here think as following: Variance = Sigma^2 = Expected Value [(x-Mu)^2] Sta...
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