Row 12309

Row ID: 12309 | Dataset Entry | Axioma AXP Content Repository

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This page contains data entry 12309 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

This discussion was actually a great exercise to repeat some formerly learned content, since i Had to Google Up again what you might mean.

I Just wanted to add that the null Hypothesis for Shapiro-wilk in Case IT can Not BE rejected actually says that the Data IS normal distributed, If IT IS rejected It May BE some other.

However If the null Hypothesis can Not BE rejected you still might have the second Order Error, or beta-error, which you can Not quantify. IT basically says that, If your null Hypothesis can Not BE rejected there IS still some posaibility that the Data you are Testing does Not behave according to your null Hypothesis to some degree of Error. Therefore you rather want to reject the null Hypothesis, AS you can define beforehand with Alpha, which degree of Error you are going to Accept. I think thats the Most important takeaway

FieldValue
text This discussion was actually a great exercise to repeat some formerly learned content, since i Had to Google Up again what you might mean. I Just wanted to add that the null Hypothesis for Shapiro-wilk in Case IT can Not BE rejected actually says that the Data IS normal distributed, If IT IS rejected It May BE some other. However If the null Hypothesis can Not BE rejected you still might have the second Order Error, or beta-error, which you can Not quantify. IT basically says that, If your nul…
label r/machinelearning
dataType comment
communityName r/MachineLearning
datetime 2024-05-20
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Raw Record

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  "text": "This discussion was actually a great exercise to repeat some formerly learned content, since i Had to Google Up again what you might mean.\n\nI Just wanted to add that the null Hypothesis for Shapiro-wilk in Case IT can Not BE rejected actually says that the Data IS normal distributed, If IT IS rejected It May BE some other.\n\nHowever If the null Hypothesis can Not BE rejected you still might have the second Order Error, or beta-error, which you can Not quantify.\nIT basically says that, If your null Hypothesis can Not BE rejected there IS still some posaibility that the Data you are Testing does Not behave according to your null Hypothesis to some degree of Error. Therefore you rather want to reject the null Hypothesis, AS you can define beforehand with Alpha, which degree of Error you are going to Accept. I think thats the Most important takeaway",
  "label": "r/machinelearning",
  "dataType": "comment",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-20",
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