survivorship bias
survival bias

Thus, it's likely we have survivorship bias in the data.

There is a survivorship bias in both fund management and trading.

A bigger problem in looking at peer groups is called survivorship bias.

Eliminating these funds' performance would create something known as 'survivorship bias'.

Another possible explanation for this phenomenon would be survivorship bias.

Think about the survivorship bias that difference can create.

The first is a statistical flaw known as survivorship bias.

Again, keep in mind that there's survivorship bias in the data.

Is it a case of survivorship bias, where the people who appear to behave just haven't made a mistake yet?

Random fluctuations and the survivorship bias exist in all fields.

It's well known that the Morningstar data suffers from survivorship bias.

Also, price action analysis can be subject to survivorship bias for failed traders do not gain visibility.

Add to that the problem of survivorship bias.

The impact on the equity risk premium from survivorship bias is negligible, about 0.1 percent.

It is closely related to the survivorship bias, which only the subjects that "survived" a process are included in the analysis.

Standard & Poor's data is corrected for survivorship bias, which has been known to skew traditional fund analysis.

In finance, survivorship bias is the tendency for failed companies to be excluded from performance studies because they no longer exist.

Survivorship bias, in which only "surviving" subjects are selected, ignoring those that fell out of view.

One is survivorship bias in the numbers.

Other interests include fund manager compensation, survivorship bias, corporate bonds, and option pricing.

For him the survivorship bias has allowed the substantive elements of the day to emerge from the confusion and hype.

In itself, this selection showed survivorship bias by excluding the likes of Russia and China.

"This highlights the importance of addressing survivorship bias in mutual fund analysis," adds Dash.

A major criticism which surfaced against his calculations was the possibility of unconscious survivorship bias in subject selections.

Survivorship bias is a type of selection bias.

These findings may reflect survival bias, with severe disease resulting in lack of qualifications and premature death, the survivor population going on to get higher qualifications.

• Possible biases: volunteer bias, selection bias, aging, the Hawthorn effect, obsequiousness, and survival biases.

Neyman's bias, or selective survival bias, is caused by excluding those who have died before the study starts, because the exposure being studied increases the risk of death.

In order to avoid survival bias (in the fame sense rather than mortality), the sample includes individuals with five years of fame at any point during the period of study.

Survivorship bias, or survival bias, is the logical error of concentrating on the people or things that "survived" some process and inadvertently overlooking those that did not because of their lack of visibility.

Among others, the WHO-commissioned studies had counted "no information about vaccination" as "unvaccinated", and they had retrospectively updated vaccine information from surviving children, while no similar update could be made for dead children, creating a so-called "survival bias" which will always produce highly beneficial effect estimates for the most recent vaccine.

As all the studies of the overall mortality effect of DTP vaccine are observational studies, they are prone to selection bias, but this selection bias would tend to work in favor of vaccinated children, and therefore the consistent observation of negative effects in studies without survival bias is worrying.