# data[].attributes.headline
Question: If you have the data of a group of entrepreneurs’ critical decisions made during the past three years and then would like to study how these decisions influence entrepreneurial outcomes. What’s the bias here?   

# data[].attributes.subject
Question: If you have the data of a group of entrepreneurs’ critical decisions made during the past three years and then would like to study how these decisions influence entrepreneurial outcomes. What’s the bias here?   

# data[].attributes.body
Those entrepreneurs with negative outcome may not be able to participate in the survey.

# data[].attributes.body
Entrepreneurs who did wrong decisions would not make the company success for three years.

# data[].attributes.body
survivorship bias, you can only do research on the companies which are still operating after making the entrepreneurial decisions

# data[].attributes.body
Only entrepreneurs that survive in the severe market for at least three years influence the outcome. However, those who failed in past three years are ignored.

# data[].attributes.body
Survivorship bias

the history data may not represent the today's situation

# data[].attributes.body
Researchers tend to pay attention to entrepreneurs who have made successful decisions with positive outcomes.

# data[].attributes.body
Survivorship bias. The researcher could only include enterpreneurs who has business survived of more than three years, and somehow ignored and exclude enterpreneur that failed within three years.

# data[].attributes.headline
In the past three years, some entrepreneurs might get help from other external factors, excluding their initial ideas.

# data[].attributes.subject
In the past three years, some entrepreneurs might get help from other external factors, excluding their initial ideas.

# data[].attributes.body
the past experience could be invalid at present

# data[].attributes.body
Survivorship bias

# data[].attributes.body
these enterprises cannot represent all the fair outcomes from the past three years that would influence right now

# data[].attributes.body
Survivorship bias. Because the data only includes decisions from entrepreneurs who are still active or successful, potentially overlooking those whose decisions led to failure or who have since exited the field.

# data[].attributes.body
The influence of one decision may delay to emerge

# data[].attributes.body
Some companies might break and got into bankruptcy shorter than 3years ,the entrepreneurs don’t have the chance to share.

# data[].attributes.body
The bias is that I'm more willing to show that the critical decisions have influences on the company outcome. And also, the recent 3 years are more important to the outcome.

# data[].attributes.body
Not only critical decisions of entrepreneurs would affect the entrepreneurial outcomes. And the result of some decisions wouldn't be seen in 3 years.

# data[].attributes.body
Survivorship bias.

# data[].attributes.body
1. these entrepreneurs can represent the whole population.

2. these decisions shared by these entrepreneurs can represent entrepreneurship in all industries and at all times.

# data[].attributes.body
The sample only include the entrepreneurs that have owned the company for at least three years. Those who didn’t but still can be referenced are not included.

# data[].attributes.body
Entreprenuers may only refer to those which are successful in decision making and ignore the wrong ones.

# data[].attributes.body
entrepreneurs have different critical decisions in different period.

# data[].attributes.body
There behavior might overwhelming in different business cycle

# data[].attributes.body
survivorship bias

only concentrated on the data of theee years but not the other years where entrepreneurial decision are being made

# data[].attributes.body
Survivorship bias.The group of entrepreneurs you have data on may not be representative of all entrepreneurs. If the sample is biased towards a specific demographic or sector, the findings may not generalize.

# data[].attributes.body
The data doesn't cover the entrepreneurs

which has not survived during the past three years.

# data[].attributes.body
The impact of certain decisions might not be immediately apparent. Studying outcomes over a short period may not capture the long-term effects of decisions made by entrepreneurs.

# data[].attributes.body
the time is changed, the data made three years ago may not be suitable nowadays. The company may change a lot during three years.

# data[].attributes.body
The data contains only the critical decisions of last three years, so the outcome may be strongly affected by the economic background instead of their own decisions.

# data[].attributes.body
The decisions may not the main factor which influences the outcomes.

# data[].attributes.body
The effects of decisions may not be immediately visible

# data[].attributes.body
The data may only contain those entrepreneurs who have critical decisions made, and the data is not the newest one. We should collect the latest data because the world environment is changing.

# data[].attributes.body
it's may not be those decisions influence the outcomes

# data[].attributes.headline
Survivorship bias. The outcomes we see are the survived ones. The CEOs may made 1 extremely smart decision, the outcome of which eclipsed the other 99 wrong decisions.

# data[].attributes.subject
Survivorship bias. The outcomes we see are the survived ones. The CEOs may made 1 extremely smart decision, the outcome of which eclipsed the other 99 wrong decisions.

# data[].attributes.body
The enterprise which can survive through three years is a typical group.

# data[].attributes.body
These corporation cannot represent the whole market, because every day, there are corporations fail and found. The corporations in the study exist different span.

# data[].attributes.body
The condition in the last 3 years is different from now.

# data[].attributes.body
suriviorship bias

# data[].attributes.body
It's survivorship bias, because only the company with the right decision can remain and be included in the study.

# data[].attributes.body
It may be involved in temporal bias. Because temporal bias arises when the time period chosen for study may not be representative of longer-term trends or changes that have occurred over a longer period of time.

# data[].attributes.body
if an entrepreneur has already fail to operate his/her company, it is difficult to collect their decisions

# data[].attributes.body
not all the critical decisions take effect in three years

# data[].attributes.body
Some critical decisions they need longer time to be influential, just focusing on the past three years might not have a representative result

# data[].attributes.body
The time window of the past three years may have encompassed specific economic cycles or market conditions, which may have affected the relationship between decisions and outcomes, making certain decisions appear more effective when in fact they may only have been the product of a specific time period.

# data[].attributes.body
not all the critical decisions take effect in three years

# data[].attributes.body
Only focus on the data of past three years is wrong. Because maybe the key decisions of the company are made in three years ago.

# data[].attributes.body
Ignore the entrepreneurs that failed in the past three years.

# data[].attributes.body
Survivorship bias

They only concentrates on the past three years timeline. But the data in the past might not be able to represent the present situation.

# data[].attributes.body
All these enterprises have survived the challenges and have made it to today.

# data[].attributes.body
The influence of certain decisions may take longer than three years to manifest in entrepreneurial outcomes, leading to underestimation of the impact of long-term strategic decisions.

# data[].attributes.body
Entrepreneurs tend not to expose their immature or failed ideas to public. Publication bias

# data[].attributes.body
The change of outcome need time. That is not up to any single decision.

# data[].attributes.body
Only entrepreneurs who survived in the three years are considered. But many failed to survive in the three years.

# data[].attributes.body
the data we can collect only come from the entrepreneurs who survived and successed in the competing environment. so we will see their decisions always influence outcomes positivly.

# data[].attributes.body
Data may overrepresent successful entrepreneurs and underrepresent failures

# data[].attributes.body
The data only covers the critical decisions during the past three years and it’s not enough to conclude their outcomes. It’s a survivorship bias.

# data[].attributes.body
Many kinds of decisions have been made during these 3 years and before.

# data[].attributes.body
They have too big data base and not very specific information if they don’t give feed back immediately

# data[].attributes.headline
Survivorship bias. The data will not collect the data of those people who did not success

# data[].attributes.subject
Survivorship bias. The data will not collect the data of those people who did not success

# data[].attributes.body
Survivorship bias. They only focused on the people who stay at the group for three years.

# data[].attributes.body
There are many factors influencing the outcome in 3 years, the decisions don’t necessarily cause such outcomes.

# data[].attributes.body
Ignore the entrepreneurs who are less than three years into their careers. Meanwhile, there may be businesses that have gone bankrupt in the last three years.

# data[].attributes.body
There is survivorship bias. The researcher could only collect data from those entrepreneurs who are successful and ignore unsuccessful entrepreneurs.

# data[].attributes.body
Ingnored thoses entrepreneurs which are not be able to provide their actual performance, because they are bankrupted during the past 3 years.

# data[].attributes.body
survivorship bias

# data[].attributes.body
lack of long-term goal decision，many decision may take time to influent the outcome

# data[].attributes.body
There may be a retrospective bias, where entrepreneurs’ recall of past decisions is influenced by current knowledge and outcomes, potentially skewing the analysis of decision-making processes.

# data[].attributes.body
Survivorship bias.The past three years may have been a special macroeconomic period , and it may only have studied successful entrepreneurial cases

# data[].attributes.body
Survivorship bias. Some decisions take a long time to prove, it could be more than 3 years.

# data[].attributes.body
Survivorship bias：We've only seen part of this company's success

# data[].attributes.headline
Survivorship bias. You can only detect the influence on entrepreneurial outcomes from those existing companies in three years, and there are many external indicators that can also influence the outcomes.

# data[].attributes.subject
Survivorship bias. You can only detect the influence on entrepreneurial outcomes from those existing companies in three years, and there are many external indicators that can also influence the outcomes.

# data[].attributes.headline
samples only include entrepreneurs who are still going on their business and ignore the group who failed to consist their companies.

# data[].attributes.subject
samples only include entrepreneurs who are still going on their business and ignore the group who failed to consist their companies.

# data[].attributes.body
the research only focus on those people who alive after three years

# data[].attributes.body
this is survivorship bias because most decisions can't exist for 3 years.

# data[].attributes.body
There are so many attributions that could influence a company's operation, not just entrepreneurs' critical decisions. The fact is very complicated.

# data[].attributes.body
undercoverage...There is a possibility that the decisions that they have made during past three years do not have any helps with their business, the crucial decision may be made few years before and consistently influencing the entrepreneurial outcomes.

# data[].attributes.body
Three years is too short for study

# data[].attributes.body
Entrepreneur only share the successful decisions in the past 3 years.The time period is too short to be targeted.

# data[].attributes.body
three years are too long,it's too successful,the samples are survivorship bias.

# data[].attributes.body
because three years decisions are too long,they ignore decision under3years.

# data[].attributes.body
the past data/ past situation cannot represent future situation.

company's survive is not only about internal decision, it also related to external factor like economy, government policy

# data[].attributes.headline
survivor biasbecause three years decisions are too long,they ignore decision under3years

# data[].attributes.subject
survivor biasbecause three years decisions are too long,they ignore decision under3years

# data[].attributes.body
because three years decisions are too long,they ignore decision under3years