# data[].attributes.body
The London suburbs have been experiencing heavy fog for the past week. According to the latest report from the Ministry of Health, the number of deaths due to the heavy fog has sharply increased to 2,800 in just one week.

# data[].attributes.body
Researchers studied 200 male in the prison and found that 8 of them had XYY syndrome and then recruited 200 males outside of the prison and found that none of them had an extra y chromosome. They thus concluded that male with XYY syndrome are more dangerous.

# data[].attributes.body
It's not necessarily the heavy fog that causes death.

# data[].attributes.body
Ministry of health may not report the actual number of death. It only mention the increased number rather than percentage or the total number of population in London.

# data[].attributes.body
Other factors that may cause deaths during that time have not been fully considered,

# data[].attributes.body
No evidence show there is causality relationship between heavy fog and sharply increased death. There might be other elements influencing.

3036533209

# data[].attributes.body
Dense fog is the main cause of death cannot be directly proven, and the increase in deaths may be caused by other factors. More concrete scientific evidence is needed to assert that the death was "caused by thick fog".

# data[].attributes.body
The death may have been caused by other factors, not necessarily the weather.

# data[].attributes.body
Without controlling for other potential factors (such as seasonal influenza or pre-existing health conditions), there is a lack of comparative data and causal validation.

# data[].attributes.body
The fog can be one of the reason to cause the death. We need to put another factor into consideration to test the connection.

# data[].attributes.body
The death rate didn’t all relate to fog

# data[].attributes.body
The death rate has no technical correlation with the fog

# data[].attributes.body
There could be other factors that led to the deaths.

# data[].attributes.body
It’s quite hard to demonstrate a person’s death is caused directly by the fog. Other reasons may lead to the death, or it just coincidentally happend during the frog.

# data[].attributes.body
The fog may not cause the death, there may be other factors

# data[].attributes.body
It may have correlations but there were nothing prove that the heavy fog will lead to death

# data[].attributes.body
It’s a correlation and causation trap

Did the 2800 people are dead for the fog? May be not

# data[].attributes.body
Besides fogs, some deaths may not be direct correlation, fog cause traffic congestion, which in turn causes more deaths.

# data[].attributes.body
the dead population is not only caused by fog，other drivens are existing

# data[].attributes.body
There were no statistics on deaths from causes other than smog.

# data[].attributes.body
The fog is not the only cause of death at same time

# data[].attributes.body
the death may cause by other factors instead of heavy fog

# data[].attributes.body
no explanation of how they define deaths caused by fog.no number of deaths aused by fog of last week.

# data[].attributes.body
There may be other factors other than the heavy fog leading to death.

# data[].attributes.body
The sample size is too small

# data[].attributes.body
Heavy fog is not the only reason to cause deaths. Even it may be only the correlation, not causality.

# data[].attributes.body
The deaths may not all be due to the heavy fog.

# data[].attributes.body
The prison sample is not representative and the overall sample size is too small

# data[].attributes.headline
There could be other factors causing the deaths which were not considered

# data[].attributes.subject
There could be other factors causing the deaths which were not considered

# data[].attributes.body
Confusion of correlation and causality.

# data[].attributes.body
The sample sizes are not big enough, and the regional reason should be took into consideration.

# data[].attributes.body
small sample size

# data[].attributes.body
200 people in prison has a larger percentage than 200 people outside the prison, so there is a data collection bias.

# data[].attributes.body
The fog and death may not constitute cause and effect relationships

# data[].attributes.body
Other events may also result in death not only the fog

# data[].attributes.body
This is a correlation instead of causation.

# data[].attributes.headline
Something else could also cause the death increase 

# data[].attributes.subject
Something else could also cause the death increase 

# data[].attributes.body
Not enough samples ,the 200 men from outside prison are not representative

# data[].attributes.body
There might be other reasons that cause these deaths

# data[].attributes.body
The selection of research subjects exhibits sampling bias, as prison populations and the general population differ in terms of social background, educational attainment, and other factors.

# data[].attributes.body
London Fog Correlation vs. Causation: Assuming the fog caused all 2,800 deaths without proof.

Lack of Baseline: No context for what a "normal" number of deaths would be.

# data[].attributes.body
The sample size is too small.

# data[].attributes.body
The scale of the samples is limited

# data[].attributes.body
No other reasons are considered

# data[].attributes.body
sample selection is not randomly

# data[].attributes.body
There’s no causality between the death and the heavy fog.

# data[].attributes.body
There may be some other reasons for the death

# data[].attributes.body
other factors will also cause death ，they don‘t constitute cause and effect relationship

# data[].attributes.body
It is difficult to conclude that fog was the sole factor. Other factors, such as cold weather, air pollution, or underlying health conditions, could contribute to the increase in deaths.

# data[].attributes.body
The direct relationship between deaths and heavy frog is unknown.

# data[].attributes.body
didnt mention the numbrr of death due to heavy fog in normal time

# data[].attributes.body
This falsely attributes 2,800 total deaths during the foggy week to the fog itself, rather than considering how many of those deaths were normal for the period and merely coincidental with the weather.

# data[].attributes.body
It might be a correlation, since there is not an explanation as to why the hevy fog would be causing the deaths. Other causes of death are also not being taken into account

# data[].attributes.body
The sample size was not large enough

# data[].attributes.body
correlation is not causality

# data[].attributes.body
The sample size of the men recruited outside the prison is not big enough

# data[].attributes.body
The dataset isn’t big enough, so the result isn’t universal

# data[].attributes.body
may be some other things cause the death

# data[].attributes.body
The samples are too concentrated

# data[].attributes.body
Appear at the same time but doesn't mean that fog is the cause

# data[].attributes.body
XYY Study Sampling Bias: Drawing a conclusion about a general population from a pre-selected, biased group (prisoners). Confounding Variables, Small Sample Size, Stigmatizing Conclusion

# data[].attributes.body
Did the Ministry of Health directly link the fog to the cause of death, or is it a statistical correlation? It's also unclear if the deaths were actually caused by conditions related to the fog, such as respiratory issues, or if other factors were involved.

# data[].attributes.body
No evidence shows that heavy fog causes death

# data[].attributes.body
Being out of prison is not necessarily less dangerous.

# data[].attributes.body
The sample size is too small

# data[].attributes.body
There are other reasons lead to the death

# data[].attributes.body
the death rate doesn't only related to the fog.

# data[].attributes.body
Maybe some data is missing or repeating. The reason of people’s death is hard to confirm. Perhaps it was due to other problems that led to the cause of death.

# data[].attributes.body
The sample size is not big enough to be representative

# data[].attributes.body
there are no evidents that the death increased because of the frog

# data[].attributes.body
too small sample

# data[].attributes.body
The increased death rate may not influenced by heavy fog since there can be other factors may be ignored.

# data[].attributes.body
There may be other causes other than the fog that increases the death rate

# data[].attributes.headline
The sample size is too small and not representative.

# data[].attributes.subject
The sample size is too small and not representative.

# data[].attributes.body
the fog is not necessary to cause the death

# data[].attributes.body
Positively correlated but no evidence of causality that the heavy fog caused the increase in death. There might be other factors.

# data[].attributes.body
there may be some other reasons

# data[].attributes.body
The samples are incomparable , and the sample size is not enough

# data[].attributes.body
Other causes of death haven’t been considered in this report

# data[].attributes.body
Previous deaths may have been caused by other causes and have not been measured

# data[].attributes.body
Only the ‘number of deaths’ is provided, without a denominator (total population) or age-standardised mortality rate. It is impossible to determine whether the risk is increasing or by how much.

Reports focus solely on the ‘worst week’, ignoring the preceding and subsequent periods (regression towards the mean).

# data[].attributes.body
Sample isn't enough to prove and not select randomly

# data[].attributes.body
it can not prove that the deaths is because of heavy frog

# data[].attributes.body
The sample size is very small, it cannot be generalised to the population.

# data[].attributes.body
The sample size of 200 was too small, so the conclusion wasn't reliable.

# data[].attributes.body
The sample is not big enough

# data[].attributes.body
Until an age-standardised, seasonally-adjusted excess mortality analysis links the rise to specific pollutants correlated with the fog episode, the 2 800 figure is not convincing evidence that the fog itself killed 2 800 people.

# data[].attributes.body
There may be some mediation effect, not just because of the heavy fog.

# data[].attributes.body
This confuses correlation with causation by assuming the XYY syndrome causes criminality, while ignoring other factors like socioeconomic status or the possibility that the syndrome's behavioral effects lead to higher arrest rates rather than higher aggression.

# data[].attributes.body
The location of the sample was not taken into account.

# data[].attributes.body
The fog may not be the real reason for the death. There May be other reasons.

# data[].attributes.body
Correlation does not imply causation: the simultaneous occurrence of the two may be influenced by other factors (such as social support, educational background, economic status, medical intervention, etc.), rather than being directly caused by XYY syndrome itself.

# data[].attributes.body
Correction and causality trap: there is no evidence show the influence of fog

# data[].attributes.body
The data size is too small and not be collected randomly

# data[].attributes.body
Sample size is relatively small.

# data[].attributes.body
The sample size is biased and too small

# data[].attributes.body
the cause of death may not be the fog