In frequentist statistics, the p-value is a function of the observed sample results (a test statistic) relative to a statistical model, which measures how extreme the observation is.
https://en.wikipedia.org/wiki/P-values
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https://www.khanacademy.org/math/statistics-probability/significance-tests-one-sample/idea-of-significance-tests/v/type-1-errors
The probability of type I error depends on the level of significance assigned by the investigator and the existence or nonexistence of a difference between the two experimental conditions. The smaller the chosen alpha value, the smaller is the likelihood of making a type I error.
https://www.openanesthesia.org/type_i_statistical_error_definition/
Type I error. A type I error occurs when one rejects the null hypothesis when it is true. The probability of a type I error is the level of significance of the test of hypothesis, and is denoted by *alpha*. Usually a one-tailed test of hypothesis is is used when one talks about type I error. Examples:
http://www.cs.uni.edu/~campbell/stat/inf5.html
At the heart of Type I error is that we. don't want to make an unwarranted hypothesis so we exercise a lot of care by minimizing. the chance of its occurrence. Traditionally we try to set Type I error as .05 or .01 - as in. there is only a 5 or 1 in 100 chance that the variation that we are seeing is due to chance. This is called the 'level of significance'. Again, there is no guarantee that 5 in 100 is rare. enough so significance levels need to be chosen carefully.
http://www.stat.berkeley.edu/~hhuang/STAT141/Lecture-FDR.pdf
Alpha is arbitrarily defined. A 5% (0.05) level of significance is most commonly used in medicine based only on the consensus of researchers. Using a 5% alpha implies that having a 5% probability of incorrectly rejecting the null hypothesis is acceptable. Therefore, other alphas such as 10% or 1% are used in certain situations.
http://www.stomponstep1.com/p-value-null-hypothesis-type-1-error-statistical-significance/
A probability value (p-value) should be chosen before collecting data, and is most typically .05 in biomedical research. A p-value of .05 means that there is a 5% chance of making a type I error. A type I error, exists if the Null Hypothesis is incorrectly rejected.
https://www.openanesthesia.org/type_i_statistical_error_definition/
Type I and Type II errors. • Type I error , also known as a “false positive”: the error of rejecting a null. hypothesis when it is actually true. In other words, this is the error of accepting an. alternative hypothesis (the real hypothesis of interest) when the results can be. attributed to chance.
http://www.stat.berkeley.edu/~hhuang/STAT141/Lecture-FDR.pdf
False Positive. Positive pregnancy test on a non pregnant patient. A level of significance of 5%, or 1 in 20, is arbitrary set. 5% chance of making a type I error. If p is probability and p <0.05, there is 5% chance that an observed difference occurred because of chance.
https://www.openanesthesia.org/type_i_statistical_error_definition/
Using Alpha (α) to Determine Statistical Significance. You may be wondering what determines whether a p-value is “low” or “high.” That is where the selected “Level of Significance” or Alpha (α) comes in. Alpha is the probability of making a Type I Error (or incorrectly rejecting the null hypothesis).
http://www.stomponstep1.com/p-value-null-hypothesis-type-1-error-statistical-significance/
what is the level of significance type error
The probability of a type I error is the level of significance of the test of hypothesis, and is denoted by alpha. The probability of a type I error is the level of significance of the test of hypothesis.