请选择 进入手机版 | 继续访问电脑版
楼主: oliyiyi
1699 2

Type III or Type 3 Error [推广有奖]

版主

泰斗

0%

还不是VIP/贵宾

-

TA的文库  其他...

计量文库

威望
7
论坛币
272091 个
通用积分
31269.1729
学术水平
1435 点
热心指数
1554 点
信用等级
1345 点
经验
383778 点
帖子
9599
精华
66
在线时间
5466 小时
注册时间
2007-5-21
最后登录
2024-3-21

初级学术勋章 初级热心勋章 初级信用勋章 中级信用勋章 中级学术勋章 中级热心勋章 高级热心勋章 高级学术勋章 高级信用勋章 特级热心勋章 特级学术勋章 特级信用勋章

oliyiyi 发表于 2016-11-14 09:53:27 |显示全部楼层 |坛友微信交流群

+2 论坛币
k人 参与回答

经管之家送您一份

应届毕业生专属福利!

求职就业群
赵安豆老师微信:zhaoandou666

经管之家联合CDA

送您一个全额奖学金名额~ !

感谢您参与论坛问题回答

经管之家送您两个论坛币!

+2 论坛币
Anybody who learns the statistics will be familiar with the concept of type I and type II error. In hypothesis testing, 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. Plainly speaking, it occurs when we are observing a difference when in truth there is none (or more specifically - no statistically significant difference).



Type II error, also known as a "false negative": the error of not rejecting a null hypothesis when the alternative hypothesis is the true state of nature. In other words, this is the error of failing to accept an alternative hypothesis when you don't have adequate power. Plainly speaking, it occurs when we are failing to observe a difference when in truth there is one. In practice, the statistical power (equals to 1 - type II error) is commonly used. Power is the probability of rejecting the null hypothesis when the null hypothesis is indeed not true (i.e., the alternative hypothesis is true).


[color=rgb(255, 255, 255) !important]



Now, there are also a concept of type III error. Fundamentally, Type III errors occur when researchers provide the right answer to the wrong question. While the term ‘type III error’ has been used in literature and presentations, the true meaning of ‘type III error’ is not clearly or consistently defined. People may use the term “type III error” to refer to different things.


In one of presentations, the type III error was used to describe those clinical trials that would have been successful but were not performed due to resource constraint


We make type III error when conclusion is not supported by the data


Type III error referring to an error by rejecting a null hypothesis but inferring the incorrect alternative hypothesis.


A type III error is where you correctly reject the null hypothesis, but it’s rejected for the wrong reason. This compares to a Type I error (incorrectly rejecting the null hypothesis) and a Type II error (not rejecting the null when you should). Type III errors are not considered serious, as they do mean you arrive at the correct decision. They usually happen because of random chance and are a rare occurrence. You can also think of a Type III error as giving the right answer (i.e. correctly rejecting the null) to the wrong question. Either way, you’re still arriving at the correct conclusion for the wrong reason. When we say the “wrong question”, that normally means you’ve formulated your hypotheses incorrectly. In other words, both your null and alternate hypotheses may be poorly worded or completely incorrect.


In a presentation slides titled “Type III and Type IV Errors: Statistical Decision-Making Considerations in addition to Rejecting and Retaining the Null Hypothesis”, type III errorwas used to refer to the wrong model, right answer and common influences on type III error would be:


  • ·         Incorrect operationalization of variables
  • ·         Poor theory (e.g., ad hoc explanations of findings)
  • ·         Mis-identifying causal architecture (Schwartz & Carpenter, 1999)

Schwartz & Carpenter (1999) The Right Answer for the Wrong Question: Consequences of Type III Error for Public Health Research

In an editorial article of Arch Surg, the type III errorwas described as “the type III error occurs whenever the conclusions drawn are not supported by the data presented”. The author presented 5 examples using the published articles.



Type III error is solving the wrong problem precisely  – from Raiffa – 1968



Solving the wrong problem is defined as a Type III error by Howard Raiffa (1968, p. 264) and Ian Mitroff (1974). Type III errors are different from Type I and Type II errors, which involve setting the significance level too high or too low in testing the null hypothesis.


USING THE METHOD OF CONTEXT VALIDATION TO MITIGATE TYPE III ERRORS IN ENVIRONMENTAL POLICY ANALYSIS



in an article by Twardowski and Misra (2013) Con: Randomized controlled trials (RCT) have failed in the study of dialysis methods:

“The second trial (the HEMO study) committed a Type III statistical errorasking the wrong question and did not bring any valuable results, but at least it did not lead to deterioration of dialysis outcomes in the USA”
Type III error — asking the wrong question and achieving the correct answer:

Kimball [7] postulated a Type III error, an error that gives the right answer to the wrong problem. A Type IV error was subsequently postulated as a type of error that solved the right problem too late [8].



in Flick, U. (2006). An introduction to qualitative research (3rd ed.). Thousand Oaks, CA: Sage.

Flick (2006), for example, discusses qualitative validity in terms of “whether researchers see what they think they see” (p. 371). Moreover, he and others (Kirk & Miller, 1986) argue that three types of error may occur as regards qualitative validity: seeing a relationship, a principle, and so on when they are not correct (Type I error); to reject them when they are correct (Type II error); and asking the wrong questions (Type III error).

Green and Tones (1999) “For debate: Towards a secure evidence base for health promotion”,

Discussion so far has essentially been concerned with assessing the outcome of interventions and has ignored the nature of the intervention itself. Type III errorrefers to rejection of the effectiveness of a programme when the programme was inadequate in terms of design or delivery. This is neatly encapsulated in the acronym GIGO – garbage in, garbage out!

Tuck et al (1986) A defence of the small clinical trial: evaluation of three gastroenterological studies


The real worry clinically is the type III error, in which a clinically significantly inferior treatment is preferred to a superior one on the basis of insufficient dataTraditional Type I and II error describe the false positive and negative rates, Type III error describes the opportunity cost of not investigating valid hypotheses due to budgetary limitations
In an article by Counsell (2002) Predicting Outcome After Acute and Subacute Stroke Development and Validation of New Prognostic Models
Predictor variables must be easy to collect (to minimize missing data), clinically relevant, and reliable.The number of variables in multiple regression analyses must also be carefully controlled. Too few variables means that important predictors may be omitted, while too many variables can result in overfitting (a type I error in which false-positive predictors are erroneously included in the model); underfitting (a type II error in which important variables are omitted from the final model); and paradoxical fitting (a type III error in which a variable that, in truth, has a positive association with the outcome is found to have a negative association).The risk of these problems increases as the ratio of outcome events to the number of predictor variables becomes smaller (the events per variable [EPV] ratio, in which the number of events is the lower figure for binary outcomes). The risk of error is especially high with EPVs 10>
In an article by Robin et al (1990) “Type 3 and type 4 errors in the statistical evaluation of clinical trials”, the type III error was referred to
Type 3 errors, then, are errors in which the risks of a given medical or public health approach is underestimated, undetected or not specifically sought, leading to an underestimate of the risk-benefit balance.”. The type 3 error was further classified as three categories: type 3A errors arise from a failure to obtain sufficient data to determine the statistical significance of a given risk in an experimental versus a control group.  Type 3-B errors involve failures to look for or detect specific risks in an experimental versus a control group. Type 3-C errors involve risks in which the harm to subjects occurs months to years after the initial use of the modality As a result, the risk-benefit ratio of the modality is seriously underestimated.


Obviously, type III error from clinical trials has greater impact on health policy and medical practice because it involves in making the right decision. While the impact of type I and type II errors are the issues within a clinical trial, the impact of type III error goes beyond a clinical trial - if a type III error is committed, we could potentially adopted a wrong practice due to insufficient information .  

二维码

扫码加我 拉你入群

请注明:姓名-公司-职位

以便审核进群资格,未注明则拒绝

关键词:Error type III err Presentation Error

已有 1 人评分学术水平 热心指数 信用等级 收起 理由
janyiyi + 3 + 3 + 3 精彩帖子

总评分: 学术水平 + 3  热心指数 + 3  信用等级 + 3   查看全部评分

缺少币币的网友请访问有奖回帖集合
https://bbs.pinggu.org/thread-3990750-1-1.html
Crsky7 发表于 2016-11-14 10:00:32 |显示全部楼层 |坛友微信交流群
居然还有type III error

使用道具

janyiyi 发表于 2016-12-5 21:08:49 |显示全部楼层 |坛友微信交流群
谢谢分享

使用道具

您需要登录后才可以回帖 登录 | 我要注册

本版微信群
加好友,备注jltj
拉您入交流群

京ICP备16021002-2号 京B2-20170662号 京公网安备 11010802022788号 论坛法律顾问:王进律师 知识产权保护声明   免责及隐私声明

GMT+8, 2024-3-28 17:05