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Get Testing Multiple Statistical Hypotheses Resulted In Spurious Associations:

Journal of Clinical Epidemiology 59 (2006) 964e969Testing multiple statistical hypotheses resulted in spurious associations: a study of astrological signs and health Peter C. Austina,b,c,*, Muhammad.

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Testing multiple hypotheses increases the risk of drawing a false-positive conclusion. We briefly discuss this phenomenon, which is often called multiple testing. Also, methods to mitigate the risk of false-positive conclusions are discussed.

In statistics, the multiple comparisons, multiplicity or multiple testing problem occurs when one considers a set of statistical inferences simultaneously or infers a subset of parameters selected based on the observed values.

If you run a hypothesis test, there's a small chance (usually about 5%) that you'll get a bogus significant result. If you run thousands of tests, then the number of false alarms increases dramatically. For example, let's say you run 10,000 separate hypothesis tests (which is common in fields like genomics).

Statistical hypothesis: A statement about the nature of a population. It is often stated in terms of a population parameter. Null hypothesis: A statistical hypothesis that is to be tested. Alternative hypothesis: The alternative to the null hypothesis. Test statistic: A function of the sample data.

Multiple testing refers to any instance that involves the simultaneous testing of more than one hypothesis. If decisions about the individual hypotheses are based on the unad- justed marginal p-values, then there is typically a large probability that some of the true null hypotheses will be rejected.

The problem with hypothesis testing is that there always a chance that what the result considers True is actually False (Type I error, False Positive). The error probability would even higher with a lot of hypothesis testing simultaneously done.

Hypothesis tests in large samples would sensibly be conducted at substantially smaller significance levels, while still retaining very good power (why would you have power of 99.99999% if you can get power of say 99.9% and drop your type I error rate by a factor of 10?).

Multiple hypothesis testing simply refers to any instance in which more than one null hypothesis is tested simultaneously. While this problem is pervasive throughout all empirical work in economics, we focus on the analysis of data from experiments in economics.

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Form Packages
Adoption
Bankruptcy
Contractors
Divorce
Home Sales
Employment
Identity Theft
Incorporation
Landlord Tenant
Living Trust
Name Change
Personal Planning
Small Business
Wills & Estates
Packages A-Z
Form Categories
Affidavits
Bankruptcy
Bill of Sale
Corporate - LLC
Divorce
Employment
Identity Theft
Internet Technology
Landlord Tenant
Living Wills
Name Change
Power of Attorney
Real Estate
Small Estates
Wills
All Forms
Forms A-Z
Form Library
Customer Service
Terms of Service
Privacy Notice
Legal Hub
Content Takedown Policy
Bug Bounty Program
About Us
Help Portal
Legal Resources
Blog
Affiliates
Contact Us
Delete My Account
Site Map
Industries
Forms in Spanish
Localized Forms
State-specific Forms
Forms Kit
Legal Guides
Real Estate Handbook
All Guides
Prepared for You
Notarize
Incorporation services
Our Customers
For Consumers
For Small Business
For Attorneys
Our Sites
US Legal Forms
USLegal
FormsPass
pdfFiller
signNow
airSlate WorkFlow
DocHub
Instapage
Social Media
Call us now toll free:
+1 833 426 79 33
As seen in:
  • USA Today logo picture
  • CBC News logo picture
  • LA Times logo picture
  • The Washington Post logo picture
  • AP logo picture
  • Forbes logo picture
© Copyright 1997-2025
airSlate Legal Forms, Inc.
3720 Flowood Dr, Flowood, Mississippi 39232