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Type One Error
Hypothesis testing is based on certain statistical and mathematical principles that allow investigators to evaluate data by making decisions based on the probability or implausibility of observing the results obtained.
However, classic hypothesis testing has its limitations, and probabilities mathematically calculated are inextricably linked to sample size.
Furthermore, the meaning of the p value frequently is misconstrued as indicating that the findings are also of clinical significance.
Finally, hypothesis testing allows for four possible outcomes, two of which are errors that can lead to erroneous adoption of certain hypotheses:
1. The null hypothesis is rejected when, in fact, it is false.
2. The null hypothesis is rejected when, in fact, it is true (type I or alpha error).
3. The null hypothesis is conceded when, in fact, it is true.
4. The null hypothesis is conceded when, in fact, it is false (type II or beta error).
Type I error occurs when you CANNOT reject the null hypothesis and type II error occurs when you reject it inappropriately. The other two outcome would be consistent with what you might look upon as true positives and true negatives.
The sample size error is extremely important for it goes to the next point of all these discussions
That is
When does statistical significance occur and not be relevant and when does statistical significance not occur and yet the actual finding prove to be of great relevance. Sample size dictates that.
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