# Skew to the Right: Understanding Positive Skewness in Statistics and Finance

Published: 2026-04-11
Author: Warren Team
URL: https://www.heywarren.com/blog/skew-to-the-right

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Skewness is one of the most useful statistical properties for understanding financial data — far more informative than the average or standard deviation alone. A right-skewed (positively skewed) distribution tells you something important about the shape of outcomes: most results cluster below average, with occasional big wins pulling the tail upward.

## What Is Right Skew?

A distribution is **right-skewed** (or **positively skewed**) when it has a long tail extending toward higher values.

**Visual identification**:
- The bulk of observations cluster on the left
- A long tail extends to the right
- Mean > Median > Mode (typically)

**Visual identification of left skew (for comparison)**:
- The bulk of observations cluster on the right
- A long tail extends to the left
- Mean < Median < Mode

**Symmetric (no skew)**:
- Normal distribution
- Mean = Median = Mode

## The Mean-Median Relationship

A simple way to detect right skew: **Mean > Median**.

![US household income is right-skewed: the mean is ~50% higher than the median because a small number of very high earners pull the average up.](data:image/svg+xml,%3Csvg%20xmlns%3D%22http%3A%2F%2Fwww.w3.org%2F2000%2Fsvg%22%20viewBox%3D%220%200%20800%20210%22%20width%3D%22800%22%20height%3D%22210%22%20role%3D%22img%22%3E%3Ctitle%3EComparison%3C%2Ftitle%3E%3Crect%20width%3D%22100%25%22%20height%3D%22100%25%22%20fill%3D%22%23f8fafc%22%2F%3E%3Ctext%20x%3D%22230%22%20y%3D%2257.5%22%20text-anchor%3D%22end%22%20font-family%3D%22system-ui%2C-apple-system%2Csans-serif%22%20font-size%3D%2214%22%20font-weight%3D%22600%22%20fill%3D%22%230f172a%22%3EMedian%20Income%3C%2Ftext%3E%3Crect%20x%3D%22240%22%20y%3D%2225%22%20width%3D%22299.6517857142857%22%20height%3D%2255%22%20rx%3D%226%22%20fill%3D%22%232563eb%22%2F%3E%3Ctext%20x%3D%22551.6517857142858%22%20y%3D%2257.5%22%20font-family%3D%22system-ui%2C-apple-system%2Csans-serif%22%20font-size%3D%2214%22%20font-weight%3D%22700%22%20fill%3D%22%232563eb%22%3E%2475K%3C%2Ftext%3E%3Ctext%20x%3D%22230%22%20y%3D%22152.5%22%20text-anchor%3D%22end%22%20font-family%3D%22system-ui%2C-apple-system%2Csans-serif%22%20font-size%3D%2214%22%20font-weight%3D%22600%22%20fill%3D%22%230f172a%22%3EMean%20Income%3C%2Ftext%3E%3Crect%20x%3D%22240%22%20y%3D%22120%22%20width%3D%22450%22%20height%3D%2255%22%20rx%3D%226%22%20fill%3D%22%237c3aed%22%2F%3E%3Ctext%20x%3D%22702%22%20y%3D%22152.5%22%20font-family%3D%22system-ui%2C-apple-system%2Csans-serif%22%20font-size%3D%2214%22%20font-weight%3D%22700%22%20fill%3D%22%237c3aed%22%3E%24112K%3C%2Ftext%3E%3C%2Fsvg%3E)

*US household income is right-skewed: the mean is ~50% higher than the median because a small number of very high earners pull the average up.*

**Why**: Outliers on the right tail pull the [arithmetic mean](/blog/arithmetic-mean-versus-geometric-mean) upward more than they pull the median. The median is resistant to extreme values.

**Example — Income distribution (strongly right-skewed)**:
- Median US household income 2023: ~$74,580
- Mean US household income 2023: ~$112,000
- The mean is 50% higher than the median because a small number of very high incomes pull the average up

This is why "median income" is reported more often than "mean income" for describing a typical household — the median is a better representation of a "typical" case in a right-skewed distribution.

## Skewness Formula

Statistical skewness has several definitions, but the most common is **Pearson's moment coefficient of skewness**:

**Skewness = E[(X − μ)³] / σ³**

Where:
- E = expected value
- μ = mean
- σ = standard deviation

**Interpretation**:
- Skewness = 0: Symmetric distribution
- Skewness > 0: Right-skewed
- Skewness < 0: Left-skewed
- Skewness > 1 or < -1: Highly skewed
- Skewness between -0.5 and 0.5: Approximately symmetric

## Common Right-Skewed Distributions

### 1. Income and Wealth

![Right skew appears across many financial and economic datasets, from income to venture capital returns.](data:image/svg+xml,%3Csvg%20xmlns%3D%22http%3A%2F%2Fwww.w3.org%2F2000%2Fsvg%22%20viewBox%3D%220%200%20760%20211%22%20width%3D%22760%22%20height%3D%22211%22%20role%3D%22img%22%3E%3Ctitle%3EHierarchy%3C%2Ftitle%3E%3Crect%20width%3D%22100%25%22%20height%3D%22100%25%22%20fill%3D%22%23f8fafc%22%2F%3E%3Crect%20x%3D%22300%22%20y%3D%2220%22%20width%3D%22160%22%20height%3D%2258%22%20rx%3D%228%22%20fill%3D%22%232563eb%22%2F%3E%3Ctext%20x%3D%22380%22%20y%3D%2254%22%20text-anchor%3D%22middle%22%20font-family%3D%22system-ui%2C-apple-system%2Csans-serif%22%20font-size%3D%2214%22%20font-weight%3D%22700%22%20fill%3D%22white%22%3ERight-Skewed%20Data%3C%2Ftext%3E%3Cpath%20d%3D%22M%20380%2078%20L%20380%20105.5%20L%20110%20105.5%20L%20110%20133%22%20stroke%3D%22%23cbd5e1%22%20stroke-width%3D%222%22%20fill%3D%22none%22%2F%3E%3Crect%20x%3D%2230%22%20y%3D%22133%22%20width%3D%22160%22%20height%3D%2258%22%20rx%3D%228%22%20fill%3D%22white%22%20stroke%3D%22%230891b2%22%20stroke-width%3D%222%22%2F%3E%3Ctext%20x%3D%22110%22%20y%3D%22158%22%20text-anchor%3D%22middle%22%20font-family%3D%22system-ui%2C-apple-system%2Csans-serif%22%20font-size%3D%2213%22%20font-weight%3D%22600%22%20fill%3D%22%230f172a%22%3EIncome%20%26amp%3B%20Wealth%3C%2Ftext%3E%3Ctext%20x%3D%22110%22%20y%3D%22176%22%20text-anchor%3D%22middle%22%20font-family%3D%22system-ui%2C-apple-system%2Csans-serif%22%20font-size%3D%2210%22%20fill%3D%22%2364748b%22%3ESkewness%20%2B1.5%20to%20%2B2.0%3C%2Ftext%3E%3Cpath%20d%3D%22M%20380%2078%20L%20380%20105.5%20L%20290%20105.5%20L%20290%20133%22%20stroke%3D%22%23cbd5e1%22%20stroke-width%3D%222%22%20fill%3D%22none%22%2F%3E%3Crect%20x%3D%22210%22%20y%3D%22133%22%20width%3D%22160%22%20height%3D%2258%22%20rx%3D%228%22%20fill%3D%22white%22%20stroke%3D%22%230891b2%22%20stroke-width%3D%222%22%2F%3E%3Ctext%20x%3D%22290%22%20y%3D%22158%22%20text-anchor%3D%22middle%22%20font-family%3D%22system-ui%2C-apple-system%2Csans-serif%22%20font-size%3D%2213%22%20font-weight%3D%22600%22%20fill%3D%22%230f172a%22%3EVC%20Returns%3C%2Ftext%3E%3Ctext%20x%3D%22290%22%20y%3D%22176%22%20text-anchor%3D%22middle%22%20font-family%3D%22system-ui%2C-apple-system%2Csans-serif%22%20font-size%3D%2210%22%20fill%3D%22%2364748b%22%3EPower%20law%20tail%3C%2Ftext%3E%3Cpath%20d%3D%22M%20380%2078%20L%20380%20105.5%20L%20470%20105.5%20L%20470%20133%22%20stroke%3D%22%23cbd5e1%22%20stroke-width%3D%222%22%20fill%3D%22none%22%2F%3E%3Crect%20x%3D%22390%22%20y%3D%22133%22%20width%3D%22160%22%20height%3D%2258%22%20rx%3D%228%22%20fill%3D%22white%22%20stroke%3D%22%230891b2%22%20stroke-width%3D%222%22%2F%3E%3Ctext%20x%3D%22470%22%20y%3D%22158%22%20text-anchor%3D%22middle%22%20font-family%3D%22system-ui%2C-apple-system%2Csans-serif%22%20font-size%3D%2213%22%20font-weight%3D%22600%22%20fill%3D%22%230f172a%22%3EStock%20Returns%3C%2Ftext%3E%3Ctext%20x%3D%22470%22%20y%3D%22176%22%20text-anchor%3D%22middle%22%20font-family%3D%22system-ui%2C-apple-system%2Csans-serif%22%20font-size%3D%2210%22%20fill%3D%22%2364748b%22%3EUnlimited%20upside%3C%2Ftext%3E%3Cpath%20d%3D%22M%20380%2078%20L%20380%20105.5%20L%20650%20105.5%20L%20650%20133%22%20stroke%3D%22%23cbd5e1%22%20stroke-width%3D%222%22%20fill%3D%22none%22%2F%3E%3Crect%20x%3D%22570%22%20y%3D%22133%22%20width%3D%22160%22%20height%3D%2258%22%20rx%3D%228%22%20fill%3D%22white%22%20stroke%3D%22%230891b2%22%20stroke-width%3D%222%22%2F%3E%3Ctext%20x%3D%22650%22%20y%3D%22158%22%20text-anchor%3D%22middle%22%20font-family%3D%22system-ui%2C-apple-system%2Csans-serif%22%20font-size%3D%2213%22%20font-weight%3D%22600%22%20fill%3D%22%230f172a%22%3EInsurance%20Claims%3C%2Ftext%3E%3Ctext%20x%3D%22650%22%20y%3D%22176%22%20text-anchor%3D%22middle%22%20font-family%3D%22system-ui%2C-apple-system%2Csans-serif%22%20font-size%3D%2210%22%20fill%3D%22%2364748b%22%3ERare%20catastrophic%20events%3C%2Ftext%3E%3C%2Fsvg%3E)

*Right skew appears across many financial and economic datasets, from income to venture capital returns.*

Perhaps the most famous right-skewed data:
- Most people earn modest incomes; a few earn enormous amounts
- US household income skewness: approximately +1.5 to +2.0
- Wealth distribution is even more skewed — Pareto distribution often provides a reasonable fit

### 2. Home Prices in a Metro Area

Most homes cluster around a median value; a few luxury homes extend the right tail. Mean home price > median home price in most US metros.

### 3. Stock Returns

Long-run equity returns are mildly right-skewed for individual stocks:
- Limited downside (can't lose more than 100%)
- Unlimited upside (some stocks have 100x+ returns)
- Individual stock distributions are typically right-skewed
- Overall index returns are closer to symmetric (portfolio diversification)

### 4. City Populations

Most cities are small; a few are enormous (NYC, LA, Chicago in US). This is a classic example of a power law distribution — an extreme form of right skew.

### 5. Startup/Venture Capital Returns

Highly right-skewed:
- Most startups fail or return minimal value
- A few home runs (Google, Facebook, Airbnb) return 100x-1000x
- The tail drives the majority of fund returns
- VC fund returns follow the "power law" more extremely than most distributions

### 6. Business Revenue Growth

Startup revenue growth in a year can be:
- Close to zero for most
- 10-20% for many mature businesses
- 100%+ for hypergrowth companies
- Occasionally 1000%+ for viral products

### 7. File Sizes, Web Page Views, Social Media Engagement

Internet data tends to be strongly right-skewed — most pages get few views, a few get millions.

### 8. Insurance Claims

Most claims are small; occasional catastrophic claims extend the tail. Insurance companies must provision for the tail risk.

## Why Right Skew Matters in Finance

### 1. Risk Management

Standard deviation (volatility) alone doesn't tell the full risk story. Two distributions with the same mean and standard deviation can have very different risk profiles:

**Symmetric distribution**: Losses and gains equally likely.

**Left-skewed distribution**: Occasional large losses (heavy left tail). This is how equity markets behave — big crashes are more common than big rallies in terms of extreme moves. Portfolio managers should be aware of this tail risk.

**Right-skewed distribution**: Occasional large gains (heavy right tail). This is attractive — limited downside with lottery-ticket upside.

### 2. Expected Value vs. Typical Outcome

In right-skewed distributions, the expected value (mean) can be much higher than the typical (median) outcome:

**Example — Venture Capital fund of 30 investments**:
- 15 lose everything (total loss)
- 10 return 1-2x investment
- 3 return 3-5x
- 2 return 20x+

The mean return might be 3x; the median is barely break-even. If you invest in only one startup, your expected outcome is much worse than the average suggests — because most outcomes are far below average.

This is why diversification matters in right-skewed return distributions. You need enough bets to capture some tail outcomes.

### 3. Option Pricing

Options have a mathematically right-skewed payoff structure:
- Limited downside (can lose only the premium paid)
- Unlimited upside (for calls) or large upside (for puts)

This right-skew is fundamental to why options exist as instruments — they transform a symmetric underlying return distribution into an asymmetric (right-skewed) payoff.

### 4. Income Tax Analysis

Progressive tax systems interact with right-skewed income:
- A small percentage of earners pay a large percentage of total income tax
- US top 1% pay ~40% of federal income taxes
- This is possible because income is so right-skewed at the top

## Visualizing Right Skew

### Histogram

A histogram of right-skewed data shows:
- Tall bars on the left (many observations in lower bins)
- Declining heights moving right
- A long tail of sparse observations extending right

### Box Plot

A box plot of right-skewed data shows:
- Median closer to the lower end of the box
- Upper whisker longer than lower whisker
- Multiple outliers above the upper whisker

### Log Transformation

A common technique for right-skewed data is **taking the logarithm**. Many right-skewed distributions (like income, stock prices) become approximately normal under log transformation.

**Examples of log transformation**:
- Log income is approximately normal
- Log market cap is approximately normal
- Log returns are approximately normal (though with fatter tails than true normal)

The prevalence of log-normal [distributions in finance](/blog/what-is-distributions) and economics is why so many calculations use log returns instead of simple returns.

## Statistical Tests for Skewness

### 1. Moment-based skewness calculation

Calculate Pearson's moment coefficient as described above.

### 2. Fisher-Pearson standardized skewness

```
G1 = m3 / m2^(3/2)
```

Where m2 is the second central moment and m3 is the third central moment.

### 3. Bowley's skewness

Uses quartiles only:
```
Skewness = (Q3 + Q1 - 2 × Q2) / (Q3 - Q1)
```

Useful when data has extreme outliers that distort moment-based calculations.

### 4. In Excel

Use `SKEW()` function:
```
=SKEW(range_of_values)
```

Returns the sample skewness — positive for right-skewed, negative for left-skewed.

### 5. In Python (Pandas)

```python
df['column'].skew()
```

### 6. Visual identification

A box plot or histogram often provides more intuitive understanding than a numerical skewness coefficient.

## Kurtosis: The Related Concept

**Kurtosis** measures the "tailedness" of a distribution — how much weight is in the tails vs. the center.

![Skewness and kurtosis together describe distribution shape beyond mean and standard deviation.](data:image/svg+xml,%3Csvg%20xmlns%3D%22http%3A%2F%2Fwww.w3.org%2F2000%2Fsvg%22%20viewBox%3D%220%200%20720%20480%22%20width%3D%22720%22%20height%3D%22480%22%20role%3D%22img%22%3E%3Ctitle%3EQuadrant%20matrix%3C%2Ftitle%3E%3Crect%20width%3D%22100%25%22%20height%3D%22100%25%22%20fill%3D%22%23f8fafc%22%2F%3E%3Crect%20x%3D%2290%22%20y%3D%2225%22%20width%3D%22300%22%20height%3D%22190%22%20fill%3D%22%23dbeafe%22%2F%3E%3Crect%20x%3D%22390%22%20y%3D%2225%22%20width%3D%22300%22%20height%3D%22190%22%20fill%3D%22%23d1fae5%22%2F%3E%3Crect%20x%3D%2290%22%20y%3D%22215%22%20width%3D%22300%22%20height%3D%22190%22%20fill%3D%22%23ffedd5%22%2F%3E%3Crect%20x%3D%22390%22%20y%3D%22215%22%20width%3D%22300%22%20height%3D%22190%22%20fill%3D%22%23ede9fe%22%2F%3E%3Cline%20x1%3D%2290%22%20y1%3D%22215%22%20x2%3D%22690%22%20y2%3D%22215%22%20stroke%3D%22%2364748b%22%20stroke-width%3D%222%22%2F%3E%3Cline%20x1%3D%22390%22%20y1%3D%2225%22%20x2%3D%22390%22%20y2%3D%22405%22%20stroke%3D%22%2364748b%22%20stroke-width%3D%222%22%2F%3E%3Ctext%20x%3D%22240%22%20y%3D%22100%22%20text-anchor%3D%22middle%22%20font-family%3D%22system-ui%2C-apple-system%2Csans-serif%22%20font-size%3D%2213%22%20font-weight%3D%22700%22%20fill%3D%22%230f172a%22%3ELeft%20%2B%20Fat%20Tails%3C%2Ftext%3E%3Ctext%20x%3D%22240%22%20y%3D%22120%22%20text-anchor%3D%22middle%22%20font-family%3D%22system-ui%2C-apple-system%2Csans-serif%22%20font-size%3D%2210%22%20fill%3D%22%2364748b%22%3E%E2%80%A2%20Equity%20crashes%3C%2Ftext%3E%3Ctext%20x%3D%22240%22%20y%3D%22136%22%20text-anchor%3D%22middle%22%20font-family%3D%22system-ui%2C-apple-system%2Csans-serif%22%20font-size%3D%2210%22%20fill%3D%22%2364748b%22%3E%E2%80%A2%20Credit%20losses%3C%2Ftext%3E%3Ctext%20x%3D%22540%22%20y%3D%22100%22%20text-anchor%3D%22middle%22%20font-family%3D%22system-ui%2C-apple-system%2Csans-serif%22%20font-size%3D%2213%22%20font-weight%3D%22700%22%20fill%3D%22%230f172a%22%3ERight%20%2B%20Fat%20Tails%3C%2Ftext%3E%3Ctext%20x%3D%22540%22%20y%3D%22120%22%20text-anchor%3D%22middle%22%20font-family%3D%22system-ui%2C-apple-system%2Csans-serif%22%20font-size%3D%2210%22%20fill%3D%22%2364748b%22%3E%E2%80%A2%20VC%20returns%3C%2Ftext%3E%3Ctext%20x%3D%22540%22%20y%3D%22136%22%20text-anchor%3D%22middle%22%20font-family%3D%22system-ui%2C-apple-system%2Csans-serif%22%20font-size%3D%2210%22%20fill%3D%22%2364748b%22%3E%E2%80%A2%20Options%20payoffs%3C%2Ftext%3E%3Ctext%20x%3D%22240%22%20y%3D%22298%22%20text-anchor%3D%22middle%22%20font-family%3D%22system-ui%2C-apple-system%2Csans-serif%22%20font-size%3D%2213%22%20font-weight%3D%22700%22%20fill%3D%22%230f172a%22%3ELeft%20%2B%20Thin%20Tails%3C%2Ftext%3E%3Ctext%20x%3D%22240%22%20y%3D%22318%22%20text-anchor%3D%22middle%22%20font-family%3D%22system-ui%2C-apple-system%2Csans-serif%22%20font-size%3D%2210%22%20fill%3D%22%2364748b%22%3E%E2%80%A2%20Carry%20trades%3C%2Ftext%3E%3Ctext%20x%3D%22540%22%20y%3D%22298%22%20text-anchor%3D%22middle%22%20font-family%3D%22system-ui%2C-apple-system%2Csans-serif%22%20font-size%3D%2213%22%20font-weight%3D%22700%22%20fill%3D%22%230f172a%22%3ERight%20%2B%20Thin%20Tails%3C%2Ftext%3E%3Ctext%20x%3D%22540%22%20y%3D%22318%22%20text-anchor%3D%22middle%22%20font-family%3D%22system-ui%2C-apple-system%2Csans-serif%22%20font-size%3D%2210%22%20fill%3D%22%2364748b%22%3E%E2%80%A2%20Lottery%20tickets%3C%2Ftext%3E%3Ctext%20x%3D%2290%22%20y%3D%22425%22%20text-anchor%3D%22start%22%20font-family%3D%22system-ui%2C-apple-system%2Csans-serif%22%20font-size%3D%2211%22%20fill%3D%22%2364748b%22%3ELeft%20Skew%3C%2Ftext%3E%3Ctext%20x%3D%22690%22%20y%3D%22425%22%20text-anchor%3D%22end%22%20font-family%3D%22system-ui%2C-apple-system%2Csans-serif%22%20font-size%3D%2211%22%20fill%3D%22%2364748b%22%3ERight%20Skew%3C%2Ftext%3E%3Ctext%20x%3D%22390%22%20y%3D%22453%22%20text-anchor%3D%22middle%22%20font-family%3D%22system-ui%2C-apple-system%2Csans-serif%22%20font-size%3D%2212%22%20font-weight%3D%22600%22%20fill%3D%22%230f172a%22%3ESkewness%3C%2Ftext%3E%3Ctext%20x%3D%2280%22%20y%3D%2237%22%20text-anchor%3D%22end%22%20font-family%3D%22system-ui%2C-apple-system%2Csans-serif%22%20font-size%3D%2211%22%20fill%3D%22%2364748b%22%3EFat%20Tails%3C%2Ftext%3E%3Ctext%20x%3D%2280%22%20y%3D%22405%22%20text-anchor%3D%22end%22%20font-family%3D%22system-ui%2C-apple-system%2Csans-serif%22%20font-size%3D%2211%22%20fill%3D%22%2364748b%22%3EThin%20Tails%3C%2Ftext%3E%3Ctext%20x%3D%2235%22%20y%3D%22215%22%20text-anchor%3D%22middle%22%20font-family%3D%22system-ui%2C-apple-system%2Csans-serif%22%20font-size%3D%2212%22%20font-weight%3D%22600%22%20fill%3D%22%230f172a%22%20transform%3D%22rotate%28-90%2035%20215%29%22%3EKurtosis%3C%2Ftext%3E%3C%2Fsvg%3E)

*Skewness and kurtosis together describe distribution shape beyond mean and standard deviation.*

- **Leptokurtic** (kurtosis > 3): Heavy tails; more extreme values than normal
- **Mesokurtic** (kurtosis = 3): Normal distribution
- **Platykurtic** (kurtosis < 3): Thin tails; fewer extreme values

Many right-skewed financial distributions are also leptokurtic (fat-tailed). Together, skewness and kurtosis characterize the shape beyond mean and standard deviation.

## Implications for Portfolio Construction

Understanding skew is valuable for portfolio construction:

**1. Position sizing for right-skewed bets**: With lottery-ticket-like distributions (VC, options), position size each bet small — you need many shots at the tail.

**2. Avoid "picking up nickels in front of a steamroller"**: Strategies with left-skewed returns (selling options, carry trades) have steady small gains but occasional catastrophic losses. Risk-adjusted returns can look great until the loss comes.

**3. Diversification requirements**: Right-skewed distributions require more diversification than symmetric ones — because the mean return comes from tail events rather than typical observations.

**4. Leverage caution**: Leverage amplifies both sides of a distribution. With left-skewed distributions, leverage can lead to ruin when the rare big loss comes.

## Authoritative Sources

For deeper background and primary-source data on this topic, the following authoritative sources are useful starting points:

- [IRS](https://www.irs.gov/)
- [SEC](https://www.sec.gov/)
- [Federal Reserve](https://www.federalreserve.gov/)

## Conclusion

Right skew is a fundamental property of many important distributions in finance, economics, and business — from income and wealth to stock returns and startup outcomes. Recognizing right-skewed data shapes how you interpret averages (mean > median), assess risks (tail events matter more), and structure decisions (position sizing, diversification). Standard tools like standard deviation don't capture skewness — for a complete picture of a distribution's shape, you need to examine skewness and kurtosis alongside mean and variance.

For related statistical and financial analysis topics, see our guides on [CAGR formula](/blog/cagr-formula), [risk averse investing](/blog/risk-averse-and-risk), and [EPS formula](/blog/eps-formula).

Warren at [heywarren.ai](https://heywarren.ai) helps investors analyze return distributions, risk profiles, and statistical properties of their portfolios.

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## Related Reading

**More from Warren**:

**Authoritative sources**:
- [SEC Investor.gov — Investing Basics](https://www.investor.gov/introduction-investing/investing-basics)
- [FINRA — Investor Education](https://www.finra.org/investors)