--- title: "Salary Statistics Fields - Complete Explanation" description: "A complete guide to salary statistics fields: min, max, average, median, quartiles, and standard deviation, for informed compensation decisions." canonical_url: "https://hrdrone.am/blog/salary-statistics-fields" --- > Index of every page on this site: [llms.txt](https://hrdrone.am/llms.txt) ## Overview This document provides a comprehensive explanation of all statistical fields returned by the Salary Statistics by Level query. Understanding these metrics will help you interpret salary data and make informed decisions about compensation, market analysis, and hiring strategies. ## Statistics Fields Reference ### Central Tendency Metrics #### `min_salary` **Type:** Integer **Description:** The lowest salary value among all applicants at this level. **What it means:** - The absolute minimum salary found in the dataset - Represents the floor of the salary range - May be an outlier if significantly lower than other values **When to use:** - Understand the lower bound of salaries - Identify minimum acceptable compensation - Set salary floors for job postings **Example:** ```text min_salary: $500 → At least one applicant at this level has a salary of $500 ``` **Interpretation tips:** - Compare with Q1 to identify if it's an outlier - If min is much lower than Q1, it may be an entry-level or part-time position - Use with max_salary to understand the full range --- #### `max_salary` **Type:** Integer **Description:** The highest salary value among all applicants at this level. **What it means:** - The absolute maximum salary found in the dataset - Represents the ceiling of the salary range - May be an outlier if significantly higher than other values **When to use:** - Understand the upper bound of salaries - Identify maximum potential compensation - Set salary caps for budget planning **Example:** ```text max_salary: $5000 → At least one applicant at this level has a salary of $5000 ``` **Interpretation tips:** - Compare with Q3 to identify if it's an outlier - If max is much higher than Q3, it may be a senior specialist or leadership role - Use with min_salary to understand the full range --- #### `avg_salary` (Average Salary) **Type:** Decimal (rounded to 2 places) **Description:** The arithmetic mean of all salaries at this level. Calculated as the sum of all salaries divided by the number of applicants. **Formula:** `SUM(salary) / COUNT(*)` **What it means:** - The "typical" salary if all salaries were equal - Sensitive to outliers (very high or very low values) - Most commonly used measure of central tendency **When to use:** - Quick overview of typical compensation - Budget planning and salary benchmarking - Comparing levels or groups **Example:** ```text avg_salary: $1750.25 → The average salary across all applicants at this level is $1750.25 ``` **Interpretation tips:** - **Compare with median:** If avg > median, high earners pull the average up - **Compare with median:** If avg < median, low earners pull the average down - **Outlier sensitivity:** A few very high salaries can significantly increase the average **Limitations:** - Can be misleading if there are extreme outliers - Doesn't show the distribution of salaries - May not represent the "typical" salary if distribution is skewed --- #### `median_salary` (50th Percentile) **Type:** Integer **Description:** The middle value when all salaries are sorted from lowest to highest. Exactly half of the applicants earn less than this value, and half earn more. **What it means:** - The "middle" salary in the dataset - Not affected by outliers (robust statistic) - Better representation of "typical" salary than average when data is skewed **When to use:** - Primary measure for understanding typical compensation - Better than average when outliers are present - Setting competitive salary offers **Example:** ```text median_salary: $1700 → Half of applicants earn less than $1700, half earn more ``` **Interpretation tips:** - **More reliable than average** when data has outliers - **Represents the 50th percentile** - middle of the distribution - **Compare with average:** Large difference indicates skewed distribution - **Best single number** to represent "typical" salary **Why it's important:** - Resistant to outliers (one very high salary won't affect it) - Represents the true middle of the data - Industry standard for salary benchmarking --- ### Distribution Metrics #### `q1_salary` (First Quartile / 25th Percentile) **Type:** Integer **Description:** The salary value below which 25% of applicants fall. Also known as the lower quartile. **What it means:** - 25% of applicants earn less than this value - 75% of applicants earn more than this value - Marks the boundary between the bottom quarter and the rest **When to use:** - Understand the lower end of the salary distribution - Identify entry-level or junior compensation - Set minimum salary expectations **Example:** ```text q1_salary: $1200 → 25% of applicants earn less than $1200, 75% earn more ``` **Interpretation tips:** - **Lower boundary** of the middle 50% (interquartile range) - **Compare with median:** Large gap indicates many low earners - **Use with Q3** to understand the spread of the middle 50% --- #### `q3_salary` (Third Quartile / 75th Percentile) **Type:** Integer **Description:** The salary value below which 75% of applicants fall. Also known as the upper quartile. **What it means:** - 75% of applicants earn less than this value - 25% of applicants earn more than this value - Marks the boundary between the top quarter and the rest **When to use:** - Understand the higher end of the salary distribution - Identify senior or experienced compensation - Set competitive salary targets **Example:** ```text q3_salary: $2200 → 75% of applicants earn less than $2200, 25% earn more ``` **Interpretation tips:** - **Upper boundary** of the middle 50% (interquartile range) - **Compare with median:** Large gap indicates many high earners - **Use with Q1** to understand the spread of the middle 50% --- #### `salary_range` **Type:** Integer **Description:** The difference between the maximum and minimum salaries. Calculated as `max_salary - min_salary`. **What it means:** - The total spread of salaries from lowest to highest - Shows the variability in compensation - Larger ranges indicate more diversity in salary levels **When to use:** - Understand salary variability within a level - Identify levels with wide compensation ranges - Assess market diversity **Example:** ```text salary_range: $4500 → The difference between highest and lowest salary is $4500 ``` **Interpretation tips:** - **Large range:** High variability, many factors affect salary - **Small range:** More standardized compensation - **Compare across levels:** Some levels may have wider ranges than others - **Use with quartiles:** Range can be misleading if outliers exist **Limitations:** - Sensitive to outliers (one extreme value affects the entire range) - Doesn't show where most salaries fall - Use interquartile range (Q3 - Q1) for a more robust measure --- #### `stddev_salary` (Standard Deviation) **Type:** Decimal (rounded to 2 places) **Description:** A measure of how spread out the salaries are from the average. Indicates the typical distance of salaries from the mean. **Formula:** Square root of the variance **What it means:** - **Low standard deviation:** Salaries are clustered close to the average - **High standard deviation:** Salaries are spread out widely - **Zero standard deviation:** All salaries are identical (rare) **When to use:** - Understand salary consistency within a level - Identify levels with high or low variability - Assess market standardization **Example:** ```text stddev_salary: $450.25 → Salaries typically vary by about $450 from the average ``` **Interpretation tips:** - **Compare with average:** If stddev ≈ 0.2 × average, moderate spread - **Compare with average:** If stddev > 0.5 × average, high variability - **Compare with average:** If stddev < 0.1 × average, low variability - **NULL value:** Occurs when there's only one data point (can't calculate spread) **Rule of thumb:** - **68% of salaries** fall within 1 standard deviation of the average - **95% of salaries** fall within 2 standard deviations of the average - **99.7% of salaries** fall within 3 standard deviations of the average **Example calculation:** ```text If avg_salary = $2000 and stddev_salary = $400: - 68% of salaries are between $1600 and $2400 - 95% of salaries are between $1200 and $2800 - 99.7% of salaries are between $800 and $3200 ``` --- ## Understanding Relationships Between Statistics ### Average vs Median **When Average > Median:** - High earners pull the average up - Right-skewed distribution (tail on the right) - Indicates some very high salaries **When Average < Median:** - Low earners pull the average down - Left-skewed distribution (tail on the left) - Indicates some very low salaries **When Average ≈ Median:** - Symmetrical distribution - Salaries are evenly distributed - Average and median both represent typical salary well ### Quartiles and Distribution Shape **Interquartile Range (IQR) = Q3 - Q1** **Small IQR:** - Most salaries are clustered in a narrow range - More standardized compensation - Less variability **Large IQR:** - Salaries are spread across a wide range - More diverse compensation - Higher variability **Symmetric Distribution:** - Q1 and Q3 are equidistant from median - (Median - Q1) ≈ (Q3 - Median) **Skewed Distribution:** - Q1 and Q3 are not equidistant from median - (Median - Q1) ≠ (Q3 - Median) ### Range vs Standard Deviation **Both measure spread, but differently:** - **Range:** Total spread (max - min), sensitive to outliers - **Standard Deviation:** Typical spread from average, less sensitive to outliers **Use Range when:** - You need to know the absolute limits - Outliers are important to understand **Use Standard Deviation when:** - You want to understand typical variability - You want a measure less affected by outliers --- ## Practical Use Cases ### Use Case 1: Setting Competitive Salary Ranges **Goal:** Determine appropriate salary range for a job posting **Recommended approach:** 1. Look at **median_salary** as the target midpoint 2. Use **q1_salary** as the minimum (25th percentile) 3. Use **q3_salary** as the maximum (75th percentile) 4. Consider **avg_salary** if distribution is symmetric **Example:** ```text Level: MIDDLE median_salary: $1700 q1_salary: $1200 q3_salary: $2200 Recommended range: $1200 - $2200 Target offer: ~$1700 (median) ``` ### Use Case 2: Identifying Market Anomalies **Goal:** Find levels with unusual salary patterns **Recommended approach:** 1. Compare **avg_salary** vs **median_salary** 2. Check **stddev_salary** for variability 3. Examine **salary_range** for outliers **Red flags:** - Large difference between average and median (> 20%) - Very high standard deviation (> 50% of average) - Extremely wide salary range - Small sample size (< 10) ### Use Case 3: Budget Planning **Goal:** Estimate total compensation costs **Recommended approach:** 1. Use **avg_salary** for total cost estimation 2. Use **median_salary** for typical cost per hire 3. Use **q3_salary** for worst-case scenario planning 4. Multiply by expected number of hires **Example:** ```text Planning to hire 5 MIDDLE level developers: avg_salary: $1750.25 Expected total: 5 × $1750.25 = $8,751.25 Worst case (using Q3): q3_salary: $2200 Worst case total: 5 × $2200 = $11,000 ``` ### Use Case 4: Salary Benchmarking **Goal:** Compare compensation across levels or groups **Recommended approach:** 1. Compare **median_salary** across levels (most reliable) 2. Compare **avg_salary** as secondary check 3. Compare **q1_salary** and **q3_salary** for distribution 4. Consider **stddev_salary** for consistency **Example comparison:** ```text JUNIOR: median: $800 avg: $850 q1: $600, q3: $1000 MIDDLE: median: $1700 avg: $1750 q1: $1200, q3: $2200 Analysis: - MIDDLE earns ~2x JUNIOR (median comparison) - Both have similar distribution shape (symmetric) - MIDDLE has wider range (more variability) ``` --- ## Common Misconceptions ### "Average is always the best measure" **Reality:** Median is often more representative, especially with outliers. **When to use average:** - Symmetric distribution - No outliers - Need for mathematical operations (sum, etc.) **When to use median:** - Skewed distribution - Presence of outliers - Need for typical value ### "Range shows where most salaries fall" **Reality:** Range shows extremes, not typical values. Use quartiles for typical range. **Better approach:** - Use Q1 to Q3 (interquartile range) for where most salaries fall - Use min/max only to understand absolute limits ### "High standard deviation is always bad" **Reality:** High variability can indicate: - Diverse skill levels within a level - Different specializations - Market flexibility - Growth opportunities **Context matters:** High variability might be expected and acceptable. ### "More data always means better statistics" **Reality:** More data helps, but data quality and representativeness matter more. **Consider:** - Is the sample representative? - Are there selection biases? - Is the data current and relevant? --- ## Statistical Significance ### Sample Size Guidelines
Sample Size Reliability Recommendation
< 10 Low Use with extreme caution, may not be representative
10-30 Moderate Use with caution, consider confidence intervals
30-100 Good Reliable for most purposes
> 100 Excellent Highly reliable, can perform detailed analysis
### Confidence Intervals (Conceptual) While not calculated in the query, understanding confidence helps: - **Larger samples:** Narrower confidence intervals, more precise estimates - **Smaller samples:** Wider confidence intervals, less precise estimates - **Rule of thumb:** ±10% margin of error for samples of 30-100 --- ## Visualizing the Statistics ### Box Plot Representation The statistics can be visualized as a box plot: **Box plot elements:** - **Whiskers:** min to max (or Q1-1.5×IQR to Q3+1.5×IQR for outliers) - **Box:** Q1 to Q3 (interquartile range) - **Line in box:** Median - **Average:** Often shown as a dot or X ### Distribution Shape Indicators **Symmetric (Normal-like):** - Average ≈ Median - Q1 and Q3 equidistant from median - Standard deviation moderate **Right-skewed (High outliers):** - Average > Median - Q3 further from median than Q1 - Standard deviation high **Left-skewed (Low outliers):** - Average < Median - Q1 further from median than Q3 - Standard deviation high --- ## Summary Table
Field Type Best For Limitations
min_salary Integer Lower bound May be outlier
max_salary Integer Upper bound May be outlier
avg_salary Decimal Quick overview Sensitive to outliers
median_salary Integer Typical value Less intuitive
q1_salary Integer Lower quartile None
q3_salary Integer Upper quartile None
salary_range Integer Total spread Sensitive to outliers
stddev_salary Decimal Variability Requires > 1 data point
--- ## Quick Reference Guide ### For Setting Salary Ranges 1. **Target:** Use `median_salary` 2. **Minimum:** Use `q1_salary` (25th percentile) 3. **Maximum:** Use `q3_salary` (75th percentile) ### For Understanding Typical Salary 1. **Primary:** Use `median_salary` (most reliable) 2. **Secondary:** Use `avg_salary` (if symmetric distribution) 3. **Check:** Compare both to assess distribution shape ### For Budget Planning 1. **Expected:** Use `avg_salary` × number of hires 2. **Conservative:** Use `q3_salary` × number of hires 3. **Optimistic:** Use `q1_salary` × number of hires ### For Market Analysis 1. **Central tendency:** Compare `median_salary` across groups 2. **Variability:** Compare `stddev_salary` across groups 3. **Distribution:** Compare quartiles (Q1, median, Q3) across groups --- ## Additional Resources - **Query Documentation:** See `salary-statistics-by-level-guide.md` - **Query File:** See `salary-statistics-by-level.sql` - **Statistical Concepts:** - Percentiles and quartiles - Measures of central tendency - Measures of variability - Distribution shapes --- ## Questions to Ask When Interpreting Results 1. **Distribution:** Is the distribution symmetric or skewed? (Compare avg vs median) 2. **Variability:** How much do salaries vary? (Check stddev and range) 3. **Outliers:** Are min/max values outliers? (Compare with quartiles) 4. **Representativeness:** Is this sample representative of the population? 5. **Context:** How do these compare to industry standards? 6. **Trends:** How do these compare across different levels or groups? By understanding these statistics and asking the right questions, you can make informed decisions about compensation, market analysis, and hiring strategies. To see how a gross figure converts to take-home pay under Armenia's 2026 rules, use the [salary calculator for 2026](https://hrdrone.am/blog/salary-calculator-armenia-2026), and compare the ranges above with what employers are advertising right now on [jobs in Armenia](https://hrdrone.am/jobs/armenia). --- This article was published by the **HR Drone** platform to contribute to the development of data-driven HR practices, salary analytics culture, and informed compensation decision-making.