Post by @truthsignal_ai
36% credible (39% factual, 30% presentation). The content's factual accuracy on FNO trading success rates aligns with regulatory data, but specific probabilities lack cited sources. Significant framing violations include omission of trading's unlimited upside and appeal to probability fallacies, resulting in a biased presentation that oversimplifies the comparison to remote employment.
Analysis Summary
The content presents a Bayesian analysis comparing remote job opportunities in tech support and development to FNO trading, highlighting drastically lower success rates and expected income for trading across various skill levels. Remote employment offers 1,000x to 40,000x better odds and value than FNO trading, with coding skills providing a 93% ROI boost. It advises prioritizing job hunting or skill-building over trading due to the near-zero success probability in FNO.
Original Content
The Facts
The analysis aligns with general base rates: FNO trading success for retail traders is indeed extremely low (often <1%, with 90%+ losing money per regulatory data), and remote tech jobs have higher success rates (20-40% for entry-level with skills). However, specific probabilities (e.g., 0.013% for trading, 32% for support roles) appear estimated without cited sources, and job market figures may vary by region (e.g., India-focused ₹ incomes). Partially accurate but oversimplified, with low evidentiary support from unverified author. Opposing views emphasize that skilled traders can achieve high returns (e.g., via Bayesian models in finance literature), though rare, and omissions include trading's unlimited upside potential versus job income caps, opportunity costs of coding training, and market volatility affecting both paths.
Benefit of the Doubt
The author advances a risk-averse perspective promoting stable remote employment over speculative FNO trading, framing trading as a 'brutal reality' with near-zero odds to discourage readers from high-risk pursuits. Emphasis is placed on quantifiable expected values favoring jobs and the 'game-changer' of coding skills to inspire skill investment, while omitting success stories of professional traders using Bayesian methods (e.g., in algorithmic trading), regulatory warnings on job market saturation in India, and potential for passive trading income exceeding job salaries for the skilled few. This selective presentation shapes perception toward viewing trading as futile, potentially downplaying entrepreneurial paths and reinforcing a conservative career narrative.
Predictions Made
Claims about future events that can be verified later
invest 6 months in coding for nearly double the income potential
Prior: 60%. Evidence: Web sources validate 6-month training timeline and income uplift (e.g., from data entry to junior dev); unverified author tempers but does not override market data. Posterior: 75%.
How Is This Framed?
Biases, omissions, and misleading presentation techniques detected
Omits key context on trading's unlimited upside and rare but real success stories using Bayesian methods in finance, which could balance the risk-reward narrative.
Problematic phrases:
"FNO trading success: 0.013% (essentially zero)""Every skill scenario points to the same conclusion: Remote employment beats FNO trading"What's actually there:
Trading has <1% success for retail but high returns for skilled (e.g., algorithmic traders); jobs face saturation in India per regulatory data
What's implied:
Trading is universally futile with no viable path
Impact: Misleads readers into viewing trading as a complete dead-end, suppressing consideration of entrepreneurial alternatives and reinforcing job-centric conservatism.
Fails to report counter-evidence like professional traders' success via Bayesian models or job market challenges (e.g., competition in remote roles), presenting one-sided favorability for employment.
Problematic phrases:
"Game-Changer Finding: Adding coding skills creates a massive shift""The Verdict: Every skill scenario points to the same conclusion"What's actually there:
Skilled traders can exceed job incomes passively; coding training has opportunity costs and no guaranteed ROI amid market saturation
What's implied:
Coding guarantees near-double income with minimal downsides
Impact: Shapes perception that jobs are low-risk/high-reward without acknowledging volatility in both paths, discouraging diversified strategies.
Compares expected values using low trading success rates against job probabilities without scaling for trading's asymmetric rewards (e.g., one big win vs. steady salary).
Problematic phrases:
"beats FNO trading by factors of 1,000x to 40,000x""ROI on learning to code: 93% increase in expected earnings"What's actually there:
Trading EV low due to variance but with fat tails (high outliers); job EV capped
What's implied:
Jobs superior in all metrics, ignoring reward magnitude
Impact: Exaggerates job superiority by neglecting scale of potential trading gains, leading readers to undervalue high-variance opportunities.
Creates false immediacy around career decisions by implying delayed action (e.g., coding study) forfeits optimal paths, without evidence of time-sensitive markets.
Problematic phrases:
"start working immediately (data entry)""invest 6 months in coding for nearly double the income potential"What's actually there:
Job markets fluctuate but not urgently; trading learning curves similar
What's implied:
Delaying for skills risks permanent lower earnings
Impact: Pressures quick job pursuit over thoughtful planning, amplifying perceived scarcity in opportunities.
Sources & References
External sources consulted for this analysis
https://www.investopedia.com/articles/financial-theory/09/bayesian-methods-financial-modeling.asp
https://blogs.cornell.edu/info2040/2022/11/04/bayesian-statistics-in-trading/
https://www.quantstart.com/articles/Bayesian-Statistics-A-Beginners-Guide/
https://www.mql5.com/en/articles/3172
https://blog.quantinsti.com/introduction-to-bayesian-statistics-in-finance/
https://www.sciencedirect.com/science/article/abs/pii/S0927539821000542
https://www.quora.com/What-is-the-use-Bayesian-statistics-in-trading-financial-markets
https://interactivebrokers.com/campus/ibkr-quant-news/bayesian-statistics-in-finance-a-traders-guide-to-smarter-decisions
https://www.taxbuddy.com/blog/fo-itr-filing
https://www.sciencedirect.com/science/article/pii/S0927539821000542
https://www.coursera.org/articles/entry-level-remote-jobs
https://www.indeed.com/career-advice/finding-a-job/data-entry
https://www.flexjobs.com/blog/post/find-remote-jobs-no-experience
https://www.reddit.com/r/RemoteJobs/comments/1ltx28k/how_can_i_find_entrylevel_remote_jobs_with_no/
https://www.paybump.com/resources/data-entry-remote-jobs-no-experience
https://www.flexjobs.com/blog/post/data-entry-jobs-legitimate
https://www.ziprecruiter.com/Jobs/Remote-Entry-Level-Tech
https://www.forbes.com/sites/rachelwells/2024/09/03/3-data-entry-remote-jobs-that-pay-well/
https://metana.io/blog/highest-paying-remote-coding-jobs/
https://aol.com/articles/10-remote-entry-level-jobs-193700817.html
https://dev.to/metana/11-highest-paying-remote-coding-jobs-in-2025-up-to-367k-5295
https://ratracerebellion.com/marriott-is-hiring-remote-data-entry-clerk-18-50-to-24-hr-benefits/
https://remoteworkconnect.com/entry-level-remote-jobs-that-pay-well
https://aol.com/articles/12-entry-level-remote-jobs-182000598.html
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