- What's the difference between NORM.INV and PERCENTILE?
- NORM.INV assumes your data follows a perfect bell curve (normal distribution) and calculates the value at a percentile based on mean and standard deviation alone—useful for modeling or when you lack raw historical data. PERCENTILE looks at your actual inventory counts and finds the exact percentile value without any distribution assumptions. Use PERCENTILE for real data; use NORM.INV for theoretical models or when distribution shape is known.
- Why does NORM.INV give an error when I use probability like 90 or 95 instead of 0.9 and 0.95?
- NORM.INV expects probability as a decimal (0.0 to 1.0), not as a percentage. The decimal 0.9 means the 90th percentile, while the number 90 is outside the valid range and triggers #NUM!. Always convert: divide your percentile by 100 (90÷100 = 0.9) or enter it as a decimal (0.9, 0.95, 0.5).
- Can NORM.INV handle negative means or negative standard deviations?
- A negative mean is perfectly valid—for example, =NORM.INV(0.5, -10, 5) works fine and returns –10. However, standard deviation must always be positive because it represents the spread or width of the distribution. Negative or zero standard deviation values return #NUM! error; the curve cannot have negative width.
- How do I use NORM.INV to set up automatic safety stock alerts?
- Create a formula that calculates the 85th or 90th percentile dynamically: =NORM.INV(0.85, AVERAGE(B2:B100), STDEV.S(B2:B100)). As you add new inventory counts to column B, the reorder threshold automatically adjusts. When actual quantity drops below this result, it's time to place a new order. This method adapts to changing supply patterns without manual updates.