# 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
sample selection is not randomly

# data[].attributes.body
They chose samples from jail, which is too small

# data[].attributes.body
No evidence has been provided to prove that the deaths of these 2,800 people were directly caused by the heavy fog.

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

# data[].attributes.body
The sample size is undercovereage

# data[].attributes.body
Sample isn't enough to prove ;

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

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

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

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

# data[].attributes.body
other factors that can also cause the problem haven‘t been considered at that time

# 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
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
too small sample

# data[].attributes.body
correlation and causality trap. The fog probably is not strongly related with death

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

# data[].attributes.body
The influence might not be so quick. There are some ignored factors related to the sharply increased death.

# 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 dead population is not only caused by fog，other drivens are existing

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

# data[].attributes.body
There may be other factors to explain their 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
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 fog and death may not constitute cause and effect relationships

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

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

# data[].attributes.body
Sampling bias

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

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

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

# 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
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
The scale of the samples are limited

# data[].attributes.body
the increasing death not only related to the fog.

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

# data[].attributes.body
didnt mention the number of death due to heavy fog one week ago or in normal time

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

# data[].attributes.body
The increase of death at that time maybe not directly related to heavy fog.Thrre

can be other factors.

# data[].attributes.body
The sample sizes are too small, and the regional reason should be took into consideration

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

# data[].attributes.body
Maybe fog wasn’t the main reason of deaths

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

# data[].attributes.body
Data on deaths from other causes are not available

# 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
It may have correlations but there were nothing prove that the heavy fog will lead to death

# data[].attributes.body
The other causes haven’t been considered in this statistics

# data[].attributes.body
No direct evidence was founded to prove there was a strong connection between fog and death

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

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

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

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

# data[].attributes.body
There are are factors causing the deaths that were not considered

# 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 death may not caused by the fog.

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

# data[].attributes.headline
There may not be a correlation between fog and death 

# data[].attributes.subject
There may not be a correlation between fog and death 

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

# data[].attributes.body
there may be effect apart from the fog that cause the death

# data[].attributes.body
they appear at the same time but may not be the cause of death

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

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

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

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

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

# data[].attributes.body
maybe the way to measure is different

# data[].attributes.body
Correlated but not direct causation

# data[].attributes.body
samples aren't comparable, the size is too small.

# data[].attributes.body
The design of this research is not specific, and what are the prisoners' crimes...? (Murder, Steal, Fruad...?)

# data[].attributes.body
The sample size is too small and may need to be compared with multiple data such as the ratio of super males to normal individuals

# data[].attributes.body
the sample is a little bit small

# data[].attributes.body
The two sample were not comparable.

# data[].attributes.body
There is no evidence to show that people outside of the prison is safer than those in the prison.

# data[].attributes.body
there is no evidence to show the correlation between the death rate and heavy fog.

# data[].attributes.body
small sample size

# data[].attributes.body
The quantity of samples is not comparable

# data[].attributes.body
Sample size... and other parameters? Need more information control.

# data[].attributes.body
The 200 males in the prison is a much smaller portion of the population comparing to 200 males outside the prison. The two sample is not comparable.

# data[].attributes.body
The sample size was not enough to make this conclusion.

# data[].attributes.body
Maybe people have an extra “Y” do have a higher likelihood to commit crimes, but there is no certain evidence that all those with an extra “Y” will definitively become criminals.

# data[].attributes.body
The sample sizes are relatively small

# data[].attributes.body
the sample size isn't big enough and also isn't random

# data[].attributes.body
The sample size is way too small and they are also not selected by random.

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

# data[].attributes.body
the population of xyy syndrome is naturally small, the sample is not large enough

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

# data[].attributes.body
XYY just has a very small proportion in male population. The sample is too small.

# data[].attributes.body
The surge of deaths may not necessarily be caused by heavy fog.

# data[].attributes.body
we can not exclude other cause of increase of death, like current flu flow.

# data[].attributes.body
the sampling method is wrong. because people in the prison of course are more dangerous.

# data[].attributes.body
the 200 males are not selected randomly

# data[].attributes.body
That’s actually persuasive. But the “population” of the sample may be too small. The number of 200 cannot prove anything.

# data[].attributes.body
The sample of prison is much smaller than outside.

# data[].attributes.body
The sample size is just 8 XYY syndrome.

# data[].attributes.body
200 males are not representative enough for the large amount of men outside the prison