Not exact matches
In statistical terms, we can reduce our «
Type II
errors» (being hedged in a rising market) only by increasing our «
Type I
errors» (being exposed to market
risk in a falling market).
In addition to the standard
types of coverage like general liability insurance or property insurance, the operational
risks that tech companies face trigger insurance needs that are solved by more nuanced lines of coverage like technology
errors and omissions insurance and cyber liability insurance.
All sorts of hilarious
errors — using one
type of data (ICD10 code data from «white healthy women» and essentially comparing the best possible data from one set of hospital data related to low -
risk births to the worst possible single set of data related to high -
risk at - home births)-- if you use the writer's same data source for hospital births but include all comers in 2007 - 2010 (not just low -
risk healthy white women), the infant death rate is actually 6.14 per 1000, which is «300 % higher death rate than at - home births!»
As the number of observations increases, so does the
risk of false positives (
type I
errors).
We account for the dependence structure of the dyadic data (i.e., the fact that each fMRI subject is involved in multiple dyads), which would otherwise underestimate the standard
errors and increase the
risk of
type 1 error20, by clustering simultaneously on both members of each dyad21, 22.
MZ twin pairs discordant for T2D are rare, and our relatively small sample size increases the
risk of
type II
errors.
To the first point: are the
errors of the innocuous, ever - present
type found in a large lender's portfolio, or egregious underwriting
errors knowingly committed to increase production while offsetting
risk through the FHA program?
A common and serious
error in the «assumption of
risk by breed» is the inability to identify individual dogs by breed, according to an established breed standard or breed
type.
I think Judy Curry has done some work on this, perhaps on the high - order
risk of
Type 1
error it implies.
Basically when we reject the null hypothesis we
risk what is called a
type 1
error.
Review and consider the
types of
errors that occur in the real estate area, and set aside time to integrate the various
risk management strategies outlined above into your practice.
Errors and omissions insurance policies vary from company to company, and are written to reflect inherent
risks and common exposures particular to different
types of businesses.
Authors suggest treating significant results in secondary outcomes with caution because of the
risk of
type I
errors from multiple significance testing.