Introduction to P-Value Trading
This article is for new traders.
Learn p-value, new traders. A p-value helps traders analyze outcomes.
P-value, what? Estimated chance of a null hypothesis-grade discovery. Trading’s null hypothesis is no correlation.
Hypotheses are tested using statistical models in trade analysis. The p-value suggests whether test findings are meaningful or random to traders.
Use an example to understand p-value. The trader wants to learn how stock price affects trading volume. Trader gets 0.03 p-value from statistical test.
Significant P-values are < 0.05. Since 0.03 is less than 0.05, the trader may reject the null hypothesis and infer stock trading volume affects price.
If the p-value is more than 0.05 (e.g., 0.10), the trader cannot reject the null hypothesis. No meaningful association between factors appears in the data.
Statistical significance does not imply size or practical impact. Statistics should be examined by traders using p-value and effect size.
Occasionally, p-values are misinterpreted. P-values exceeding 0.05 do not prove the null hypothesis or invalidate the finding. Insufficient evidence to reject the null hypothesis.
Remember that statistical analysis assumptions impact p-values. Breaking these assumptions may skew p-value significance.
Trading assesses importance by p-value. This helps traders identify random data connections and effects. Impact magnitude and p-values should be carefully assessed by traders.
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