How do you interpret raw data?

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In summary: Frequentist - me at work and school and sometimes during introspection (your memory is a terrible sample space though, methinx, but maybe there's a reason some events stick out more than others, we call it 'weighting' in statistics, *chuckle*).Nomothetic - I practice this a lot, sometimes even at work, but I never rely solely on it, and investigate as a Frequentist before.Pyrrhonian - I go here sometimes.

Mode of Inference

  • Ætiologic - isolates mechanisms for direct causation

    Votes: 0 0.0%
  • Bayesian - tends to include extraneous a priori considerations

    Votes: 0 0.0%
  • Pyrrhonian - will doubt anything, even skepticism itself

    Votes: 0 0.0%
  • Solipsist - ascribes perceptual qualia exclusively to the mind

    Votes: 0 0.0%

  • Total voters
    2
  • #1
shadowpuppet
30
0
Specifically, how do you come to believe what you do? Common methods of justifying novel premises epistemologically tend to favor one of two ideologies:

  • Stochastic - likes to interpret according to high recurrence and correlativity
  • Fatalist - believes that future/past can be deduced from knowledge of present circumstances

These are the two ideological premises from which the six poll methods can be derived: Ætiologic, Nomothetic, and Solipsist arguments can be seen primarily as assertions of determinism, whereas Bayesian, Frequentist, and Pyrrhonian frames of reference always adhere to a statistical approach. There is also another undercurrent running in this poll: Frequentist and Ætiologic justifications tend to employ an exclusively empirical underpinning; Nomothetic and Bayesian inferences depend heavily on a priori convictions (Solipsism is also usually defended using the a priori because a posteriori attempts at verification are not widely credited; Pyrrhonism may seem like an analytic proposition at first but it actually only indoctrinates an inductive negation of premises, including the self-negation of any premise that might eventually support a rationalist Pyrrhonian criterion, and so is actually an [anti-] empirical enterprise - I have taken great pains to clarify this in the past; you can hear my detailed arguments http://youtube.com/watch?v=mdEreZjrNeM").
 
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  • #2
I don't really justify much, I have a healthy mix of comfort and obligation in my work (i.e. I do what I want and what it takes to get by):

Ætiologic - isolates mechanisms for direct causation
Bayesian - tends to include extraneous a priori considerations
Frequentist - respects only empirical evidence and probability
Nomothetic - uses an intuitive system of diagnostic references
Pyrrhonian - will doubt anything, even skepticism itself
Solipsist - ascribes perceptual qualia exclusively to the mind

Ætiologic - don't use, I think it's politicians?
Bayesian - politicians...?

Frequentist - me at work and school and sometimes during introspection (your memory is a terrible sample space though, methinx, but maybe there's a reason some events stick out more than others, we call it 'weighting' in statistics, *chuckle*).

Nomothetic - I practice this a lot, sometimes even at work, but I never rely solely on it, and investigate as a Frequentist before.

Pyrrhonian - I go here sometimes.

Solipsist - it's an interesting point that has it's merits and I often consider it in a philosophical atmosphere, but I'm not a 'brain in a vat'.
 
  • #3
Bayesian / Frequentist
 
  • #4
Pythagorean said:
I don't really justify much, I have a healthy mix of comfort and obligation in my work (i.e. I do what I want and what it takes to get by):

Ætiologic - don't use, I think it's politicians?
Bayesian - politicians...?

Frequentist - me at work and school and sometimes during introspection (your memory is a terrible sample space though, methinx, but maybe there's a reason some events stick out more than others, we call it 'weighting' in statistics, *chuckle*).

Nomothetic - I practice this a lot, sometimes even at work, but I never rely solely on it, and investigate as a Frequentist before.

Pyrrhonian - I go here sometimes.

Solipsist - it's an interesting point that has it's merits and I often consider it in a philosophical atmosphere, but I'm not a 'brain in a vat'.

I agree with your description of solipsism, but I am not sure what you mean by 'politicians' and I think that the activation of memory has a lot to do with long-term potentiation and parallel connectivity in the hippocampus and cortex. However, if you primarily consider yourself to be a Frequentist, please remember to vote for it in the poll.

Moridin said:
Bayesian / Frequentist

Bayesian is a statistical form of rationalism and Frequentist is a statistical form of empiricism. Do you tend to prefer to theorize in your mind (Bayesian) or would you rather experiment in the real world (Frequentist)? Don't forget to vote!
 
  • #5
I voted nomothetic because I am always thinking like a physicist, I dislike frequentist and Ætiologic deduction (although it reminds me of engineering), I doubt the validity of bayesianism applied to reality, and I favor Wittgenstein's rejection of Pyrrhonism and Solipsism.
 
  • #6
Bayesian is a statistical form of rationalism and Frequentist is a statistical form of empiricism. Do you tend to prefer to theorize in your mind (Bayesian) or would you rather experiment in the real world (Frequentist)? Don't forget to vote!

I don't think they are mutually exclusive.
 
  • #8
shadowpuppet said:
I agree with your description of solipsism, but I am not sure what you mean by 'politicians' and I think that the activation of memory has a lot to do with long-term potentiation and parallel connectivity in the hippocampus and cortex. However, if you primarily consider yourself to be a Frequentist, please remember to vote for it in the poll.

Yeah, my answers were a bit terse and I see a grammar error even. I meant that these seem like methods meant for making big conclusions that are inevitably at the mercy of politics.

Here's your definitions:

Ætiologic - isolates mechanisms for direct causation
Bayesian - tends to include extraneous a priori considerations

I might have a misunderstanding about Baeyesian, but showing causation is often associated with political motivation in my mind; extraneous a priori considerations reminds me of a large set of data that you've made assumptions about in order to arrive at your conclusion about causation.
 

1. What is the process of interpreting raw data?

Interpreting raw data involves analyzing and making sense of the information collected or produced in its original form. This process includes organizing, cleaning, and transforming the data into a format that is easier to understand and draw conclusions from.

2. What tools or methods are used to interpret raw data?

There are various tools and methods used to interpret raw data, including statistical analysis, data visualization, and data mining. Other techniques such as trend analysis, regression, and clustering can also be used depending on the type of data and the research question.

3. How do you ensure the accuracy of the data during the interpretation process?

To ensure the accuracy of the data during interpretation, it is important to verify the data sources, check for any errors or inconsistencies, and use multiple methods for analysis. It is also crucial to have a clear understanding of the data and its limitations.

4. How do you account for bias or outliers in the data during interpretation?

Bias and outliers can significantly affect the interpretation of data. To account for bias, it is important to have a diverse sample and to use multiple data sources. Outliers can be identified and addressed by using appropriate statistical methods or by removing them from the dataset.

5. How can the interpretation of raw data be communicated effectively?

The interpretation of raw data can be communicated effectively through data visualization, such as charts, graphs, and tables. Additionally, creating a narrative or story around the data can help convey the key findings and insights. It is also important to use clear and concise language to explain the interpretation of the data.

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