Error Rate Statistics Definition
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false positives and false negatives. In statistical hypothesis testing, a type I error is the incorrect rejection of a true null hypothesis (a "false positive"), while a type II error is incorrectly retaining a false null hypothesis (a "false margin of error statistics definition negative").[1] More simply stated, a type I error is detecting an effect that is
Standard Error Statistics Definition
not present, while a type II error is failing to detect an effect that is present. Contents 1 Definition 2 Statistical test theory sampling error statistics definition 2.1 Type I error 2.2 Type II error 2.3 Table of error types 3 Examples 3.1 Example 1 3.2 Example 2 3.3 Example 3 3.4 Example 4 4 Etymology 5 Related terms 5.1 Null hypothesis 5.2
Bit Error Rate Definition
Statistical significance 6 Application domains 6.1 Inventory control 6.2 Computers 6.2.1 Computer security 6.2.2 Spam filtering 6.2.3 Malware 6.2.4 Optical character recognition 6.3 Security screening 6.4 Biometrics 6.5 Medicine 6.5.1 Medical screening 6.5.2 Medical testing 6.6 Paranormal investigation 7 See also 8 Notes 9 References 10 External links Definition[edit] In statistics, a null hypothesis is a statement that one seeks to nullify with evidence to the contrary. Most commonly it is a statement that error rate statistics sample size the phenomenon being studied produces no effect or makes no difference. An example of a null hypothesis is the statement "This diet has no effect on people's weight." Usually, an experimenter frames a null hypothesis with the intent of rejecting it: that is, intending to run an experiment which produces data that shows that the phenomenon under study does make a difference.[2] In some cases there is a specific alternative hypothesis that is opposed to the null hypothesis, in other cases the alternative hypothesis is not explicitly stated, or is simply "the null hypothesis is false" – in either event, this is a binary judgment, but the interpretation differs and is a matter of significant dispute in statistics. A typeI error (or error of the first kind) is the incorrect rejection of a true null hypothesis. Usually a type I error leads one to conclude that a supposed effect or relationship exists when in fact it doesn't. Examples of type I errors include a test that shows a patient to have a disease when in fact the patient does not have the disease, a fire alarm going on indicating a fire when in fact there is no fire, or an experiment indicating that a medical treatment should cure a disease when in fact it does not. A typeII error (o
Value (and False Discovery Rate), Negative Predictive Value (and False Omission Rate). The Four Ratios of Ratios: Likelihood Ratios for Positive Tests, Negative Tests, Positive Subjects, Negative Subjects. The Test As a
Human Error Rate Statistics
Whole: Significance, Power. The Four Fundamental Numbers In Tabular Form For individuals tested
What Is The Definition Of Type I Error
for some condition, disease, or other attribute: Doesn't Have The Condition (Satisfies Null Hypothesis) Has The Condition (Does Not Satisfy stats definition Null Hypothesis) Tests Negative (Null Accepted) True Negative TN or n00 False Negative FN or n10 Tests Positive (Null Rejected) False Positive FP or n01 True Positive TP or n11 In Words True https://en.wikipedia.org/wiki/Type_I_and_type_II_errors positive The individual has the condition and tests positive for the condition The individual does not satisfy the null hypothesis and the test rejects the null hypothesis TP = n11 = number of such individuals True negative The individual does not have the condition and tests negative for the condition The individual satisfies the null hypothesis and the test accepts the null hypothesis TN = http://www.cs.rpi.edu/~leen/misc-publications/SomeStatDefs.html n00 = number of such individuals False positive The individual does not have the condition but tests positive for the condition The individual satisfies the null hypothesis but the test rejects the null hypothesis FP = n01 = number of such individuals False negative The individual has the condition but tests negative for the condition The individual does not satisfy the null hypothesis but the test accepts the null hypothesis FN = n10 = number of such individuals The Four (or Eight) Basic Ratios Each of these four fundamental numbers can be divided by its row sum or its column sum. This gives eight basic ratios, though they come in pairs that sum to one. Sensitivity The chance of testing positive among those with the condition The chance of rejecting the null hypothesis among those that do not satisfy the null hypothesis 1 - Type II Error TP / (TP + FN) = n11 / (n10 + n11) Specificity or Selectivity The chance of testing negative among those without the condition The chance of accepting the null hypothesis among those that satisfy the null hypothesis 1 - Type I Error TN / (TN + F
Tour Start here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta Discuss the workings and policies of this site About Us Learn more about Stack Overflow the company Business Learn more about hiring http://stats.stackexchange.com/questions/1610/is-there-a-way-to-remember-the-definitions-of-type-i-and-type-ii-errors developers or posting ads with us Cross Validated Questions Tags Users Badges Unanswered Ask Question _ Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. Join them; http://www.abs.gov.au/websitedbs/a3121120.nsf/home/statistical+language+-+types+of+error it only takes a minute: Sign up Here's how it works: Anybody can ask a question Anybody can answer The best answers are voted up and rise to the top Is there a way to remember the definitions of Type error rate I and Type II Errors? up vote 64 down vote favorite 32 I'm not a statistician by education, I'm a software engineer. Yet statistics comes up a lot. In fact, questions specifically about Type I and Type II error are coming up a lot in the course of my studying for the Certified Software Development Associate exam (mathematics and statistics are 10% of the exam). I'm having trouble always coming up with the right definitions for Type I and Type II error rate statistics error - although I'm memorizing them now (and can remember them most of the time), I really don't want to freeze up on this exam trying to remember what the difference is. I know that Type I Error is a false positive, or when you reject the null hypothesis and it's actually true and a Type II error is a false negative, or when you accept the null hypothesis and it's actually false. Is there an easy way to remember what the difference is, such as a mnemonic? How do professional statisticians do it - is it just something that they know from using or discussing it often? (Side Note: This question can probably use some better tags. One that I wanted to create was "terminology", but I don't have enough reputation to do it. If someone could add that, it would be great. Thanks.) terminology type-i-errors type-ii-errors share|improve this question edited May 15 '12 at 11:34 Peter Flom♦ 57.4k966150 asked Aug 12 '10 at 19:55 Thomas Owens 6161819 Terminology is a bit vague. I changed error to typeI-errors and typeII-errors. Hope that is fine. Also, your question should be community wiki as there is no correct answer to your question. –user28 Aug 12 '10 at 20:00 @Srikant: in that case, we should make questions like this cw as well: stats.stackexchange.com/questions/22/…. –Shane Aug 12 '10 at 20:01 Older literature calls H2 the
Statistical Language - Types of Error Menu Understanding Statistics Draft Statistical Capability Framework Statistical Language ABS Presents...Videos Statistical Skills for Official Statisticians A Guide for Using Statistics for Evidence Based Policy Statistics - A Powerful Edge! ABS Sports Stats ABS Training Types of Error What is error? Error (statistical error) describes the difference between a value obtained from a data collection process and the 'true' value for the population. The greater the error, the less representative the data are of the population. Data can be affected by two types of error: sampling error and non-sampling error. What is sampling error? Sampling error occurs solely as a result of using a sample from a population, rather than conducting a census (complete enumeration) of the population. It refers to the difference between an estimate for a population based on data from a sample and the 'true' value for that population which would result if a census were taken. Sampling errors do not occur in a census, as the census values are based on the entire population. Sampling error can occur when: the proportions of different characteristics within the sample are not similar to the proportions of the characteristics for the whole population (i.e. if we are taking a sample of men and women and we know that 51% of the total population are women and 49% are men, then we should aim to have similar proportions in our sample); the sample is too small to accurately represent the population; and the sampling method is not random. Sampling error can be measured and controlled in random samples where each unit has a chance of selection, and that chance can be calculated. In general, increasing the sample size will reduce the sample error. What is non-sampling error? Non-sampling error is caused by factors other than those related to sample selection. It refers to the presence of any factor, whether systemic or random, that results in the data values not accurately reflecting the