Critical Thinking and Pandemics X: Fales of Generalization

I looked at the last general attempt and its availability by referring to many lacks that may occur when reasoning and generalizing pandemics. The most common are general generalization, to go to anecdote evidence and generalize. I will see each of them in terms of pandemic.

General generalization occurs when a person draws a conclusion about a population based on a sample based on a large enough sample. Has this form:

1. Premise: Sample s (everything too small) It is taken from P. population.

2. Premise: The observed A% SX% B.

Conclusion: % A’s% BOPERATES BOPISTERS.

In the previous attempt I presented a rough guide to the size of the sample, error and confidence level margin. In this context, this falsity is not a great consequence of the sample when it comes to ensuring confidence in the conclusion. In the case of a pandemic, a major generality involves sorting the mortality of pathogen. To do this is easy math, but the challenge is to achieve appropriate information.

During the Covid-19 pandemic, there were large samples of contaminated people. Therefore, the inferences of these large samples will not be generalized for the letal virus. But avoiding that gap does not mean that generalization is another thing that is good.

The inferences taken from relatively small samples were also generally from the treatments that are testing. For example, the initial samples of people treated with hydroxikocally treated COVID-19 were at risk of generalizing these samples. This does not mean that small samples are always useless, but it must be carefully done when generalizing them.

As a practical guide, based on pandemic (or anything), in general, when you listen to claims based on generalizations based on generalizations, the effects are supported through a properly sample sample. Being a small sample does not conclude that the conclusion is false (inference would fall), but the conclusion would not be properly supported. While a small sample gives weak logical support, an anecdote can have a great psychological force, which leads to a lease similar to general pressure.

Anecdotal evidence is committed to a commitment when a person draws a conclusion based on an anecdote (a story). Income is also committed to someone who rejects sensitive statistical data in favor of a small example or example that go against the claim. There are two forms for this Fallacy:

A form

1. Premise: Anecdote is told about a member of a P population (or small number of members).

2. Premise: The anecdote says that M (or not) c.

Conclusion: Therefore, the truth of the population of C is (or not).

Two forms

1. Premise: There are good statistical evidence cc

2. Premise: A Anecdote is an exception or to go against the general claim.

Conclusion: C is false.

This fallacy is generally like generalizing, to conclude that inference is too small. A difference between general generalization and anecdotal evidence is to use anecdotal evidence as a sample (anecdote) as a sample. It can be difficult in the desert to distinguish general generality of anecdotal evidence. Fortunately, the most important thing is to recognize that it is happening. It is much clearer that the paradigm of anecdotal evidence that the paradigm of anecdotal evidence involves rejection of statistical data in the anecdote.

People often fall victim of this falsehood, as the anecdotes have more psychological strength than statistical data. Being an anecdote that was true fails. During the Covid-19 pandemia, healing or preventing or preventing or preventing disease, or the same will happen in the next pandemia. Even if the anecdotes are lies, they do not provide a proper basis to draw conclusions about the general population. This is because the sample is not enough to ensure the great conclusion. As a concrete example, while positive anecdotes about hydroxikocally. Then, thinking of thought and Trump claims some people were accepting the anecdotes as appropriate evidence, but it was a bad reason.

Appeals for anecdotal evidence frequently occur in the context of causal reasoning, such as hydroxikocally, which adds additional complexities.

As with any falty, it is not followed that the appeal of anecdotal evidence is false. Errors are accepting conclusions based on inappropriate consequences, not making a false claim. It is also worth noting that anecdotal evidence may be useful for additional research, but not enough to prove the general claim.

As mentioned earlier, there were large samples of contaminated people without the lack of general generalization. But large samples can also be problems. This is because samples must be enough and great representatives. This leads us to the falsity of fairness.

This fallaze is committed, when a person draws a population effect, based on a sample based on a sample or prevent the effect of protecting the effects properly.

1. Premise: Sample s (this is naughty) It is taken from P. population.

2. Premise: The observed A% SX% B.

Conclusion: % A’s% BOPERATES BOPISTERS.

The problem with a twisted sample is that it does not represent the population properly and so does not support the effects properly. That is, a twisted sample is different because of the important ways of the population affecting Percent of B.

In Covid-19 cases there were serious problems with twisted samples, even with improved situations. I will be based on the general induction of virus lethality.

It is easy to calculate mortality math, but the main challenge is to be sorted by how many people are infected. Since the beginning of Pandemia, the United States had a shortage of test kit. Therefore, many of the available tests were used to the symptoms that show symptoms or those who are contaminated. This sample was a great sample but was naughty: they already showed symptoms and were lost but asymptomatic but asymptomatic people.

If we face similar situations in the next pandemic, the samples will probably have a higher mortality rate than the mortality rate. To use a simple fiction example: imagine the population of 1000 people and is infected with 200 viruses. 20 of the infected 200 people showed symptoms and have been tested only. 2 of the tested people killed 2. This sample would show a 10% mortality rate. But the real mortality rate would be 2nd 2, which is 1%. This would still be bad, but not as bad as the naughty sample would indicate. The wide test shows one of the many important reasons: it is critical to establish an accurate rate of letality. A detailed mortality rate is essential to take rational decisions about our reply to any pandemians.

As a final point, it is important to remember that mortality varies between groups in general population, based on death data. But to determine the lodality of each group, the samples used to calculate should be indicative of the population. Although it is important for general mortality, it also requires the adoption of rational decisions to know the lethality for various groups. For example, pathogens tend to be more deadly for the elderly and would need more protection in the next pandemia.

As always, I will be sure and see you in the future.