{"id":3160,"date":"2025-08-14T09:00:00","date_gmt":"2025-08-14T07:00:00","guid":{"rendered":"https:\/\/getnave.com\/blog\/?p=3160"},"modified":"2025-08-13T10:51:38","modified_gmt":"2025-08-13T08:51:38","slug":"myths-of-probabilistic-forecasting","status":"publish","type":"post","link":"https:\/\/getnave.com\/blog\/myths-of-probabilistic-forecasting\/","title":{"rendered":"4 Myths of Probabilistic Forecasting"},"content":{"rendered":"
\u00a0<\/div>\n

Probabilistic forecasting is an approach that helps teams produce dependable delivery predictions based on their past performance data. A lot of teams, however, still question whether they should use this method.\u00a0Today, we\u2019ll dispel four of the most widely spread myths of probabilistic forecasting, which all-too-often prevent teams from embracing the advantages of the most reliable approach to making delivery commitments.<\/p>\n

4 Myths of Probabilistic Forecasting<\/h2>\n

Let\u2019s separate the misconceptions from the facts.<\/p>\n

Myth #1: We Need Large Amounts of Data to Perform Probabilistic Forecasting<\/h3>\n

The fact that probabilistic forecasts are based on your past performance data doesn\u2019t mean that you need a ton of data in order to come up with reliable delivery predictions. Whether you have been collecting data from the very beginning of your board creation, or you are just getting started with new teams<\/a> is beside the point.<\/p>\n

The main prerequisite of producing reliable forecasts is to maintain a stable system.<\/strong> If your delivery workflow is optimized for predictability, you will need 20 to 30 completed items to come up with accurate results. It\u2019s not about quantity. It\u2019s all about taking control of your management practices and ensuring you deliver results in a consistent manner.<\/p>\n

If your system doesn\u2019t produce the results you are hoping for and you\u2019d like to explore the proven roadmap to optimize your delivery systems for predictability, I\u2019d be thrilled to welcome you to our Sustainable Predictability<\/a> program.<\/p>\n

Myth #2: Probabilistic Forecasting Only Works with Items of the Same Size<\/h3>\n

Story sizing into even pieces<\/a> is a widely-spread activity, which is often considered to be a prerequisite to making reliable future predictions. This is one of the biggest myths of probabilistic forecasting.<\/p>\n

The concept of artificially splitting your work items into even pieces to be able to produce an accurate delivery forecast is not valid. In fact, resizing your stories is not only completely irrelevant to forecasting, but it can also have a negative effect on the goals you\u2019re trying to achieve.<\/p>\n

The main prerequisite to making accurate delivery forecasts lies in maintaining a thin-tailed distribution<\/a>. Stable systems produce thin-tailed distributions.<\/p>\n

In a stable system, you will probably have items of different sizes, and the matter of how fast they are released will only depend on their priority. It\u2019s the urgency of the items that matters the most. If your items have the same priority, they have to be processed in a FIFO manner.<\/p>\n

Even if you have to split your work items into smaller pieces of work, you should always strive to come up with potentially releasable increments that still bring customer value.Once again, that will not have any influence on the accuracy of your probabilistic forecast.<\/strong><\/p>\n

The basis of your forecasts contains work items of different sizes. There is no need to estimate or compare your items – the effort time you need to complete your work does not equate to delivery time. 60% to 99% of your delivery time is waiting time and this is something you cannot estimate. That’s what makes the probabilistic forecasting approach way more reliable than deterministic estimating<\/a>; it takes into account all the variability in your system.<\/p>\n

\u00a0<\/div>\n

Myth #3: Probabilistic Predictions Are Difficult to Interpret and Use<\/h3>\n

No, they aren\u2019t, and you don\u2019t need to have a Master\u2019s degree in math to understand them. There are tools at your disposal that will help you produce accurate probabilistic forecasts. One such tool is the Monte Carlo simulation<\/a>.<\/p>\n

The simulation uses a large number of random trials based on past throughput data to predict the throughput for a future time frame. You define the start date and the number of tasks and the simulation provides a range of delivery dates and the probability that comes with each date. For any date in the future, it uses the throughput of a random day in the past in order to simulate how many work items are likely to get done.<\/p>\n

For example, let\u2019s say on Sep 10th, you\u2019ve had a throughput of 6 tasks. The simulation takes this number and assumes that this is how many assignments will be completed on Apr 14th. To project the probable throughput of Apr 15th, it takes the throughput of another random day in the past and so on.<\/p>\n

\"Monte<\/a><\/p>\n

The simulation is repeated tens of thousands of times before the results are presented in the form of a probability distribution with percentiles increasing from left to right. In this simulation, we set a backlog of 78 tasks and we want to start working on it on Apr 14th. The simulation tells us that there is an 85% probability that we can finish all the backlog items by Jul 21st. The further you go in time, the greater the certainty of completing all the tasks.<\/p>\n

Myth #4: Probabilistic Forecasting Doesn’t Account For Story Splitting<\/h3>\n

Let\u2019s take a step back. Just because you have 200 stories in your backlog, this doesn\u2019t mean that these exact 200 stories will be delivered on the date you\u2019ve committed. That\u2019s not what the Monte Carlo simulation is telling you. What the simulation is telling you is \u201cIf you have a budget for 200 items, they will be done by date X and there is Y% certainty that you\u2019ll achieve that goal\u201d.<\/strong><\/p>\n

You\u2019ll probably split your stories, some of them will drop off, more will be added, you\u2019ll discover defects and additional work will come in between. You can take any 200 items you want, the simulation Monte Carlo produced will still be valid.<\/p>\n

Story splitting is about determining whether something is more complex than we initially assumed. If you split your initial story into 3 other stories, that doesn\u2019t necessarily mean that you\u2019ll work on all the 3 new stories. When it comes to story splitting, the most important part is to propose the most feasible option that will still solve your customer\u2019s problem.<\/p>\n

Furthermore, the fact that you\u2019ve come up with an initial delivery commitment doesn\u2019t mean your job is done. The release planning<\/a> phase is just the beginning. Don\u2019t fall into the trap of assuming that everything will go as planned.<\/p>\n

Once you begin your work and start delivering results, you should continuously reevaluate your forecast<\/a> and adjust your course<\/a> accordingly. You need to look into your plan from a continuous perspective and decide over time how best to fill the spots for these 200 items.<\/p>\n

Moreover, your delivery rate will vary based on the knowledge discovery process<\/a>, any changes in your team setup or the stability of your workflow. All these factors will affect the base you used to perform your initial prediction. That\u2019s why continuous forecasting is essential \u2013 to be able to deliver on time, you need to make sure that you have your finger on the pulse of the project.<\/p>\n

If you\u2019re willing to achieve sustainable predictability in your delivery workflows, this roadmap is your ticket to make that happen. In fact, this is the exact roadmap we explore in-depth in our\u00a0Sustainable Predictability<\/a>\u00a0digital course and I\u2019d be thrilled to welcome you to the program.<\/p>\n

\u00a0<\/div>\n

The Advantages of Forecasting<\/h2>\n

Here at Nave, we have more than 10,000 customers who use probabilistic forecasting to either plan their next releases or predict the delivery date of a project with a fixed scope. This method brings more value than the traditional estimating approach in so many different ways.<\/p>\n

Probabilistic forecasting has proven to be way more accurate than deterministic estimation, as it uses models based on your own past performance data. It doesn\u2019t rely on intuition or gut feeling.<\/p>\n

It\u2019s much less time & effort-consuming. We\u2019ve seen how projects spanning greater than 1 year and costing in excess of $10,000,000 have taken less than 1 day using only a couple of people to analyze the data to build a reliable, high-quality forecast.<\/strong><\/p>\n

Probably the biggest advantage of this approach is the fact it\u2019s not deterministic. Forecasting clearly defines the risk associated with certain outcomes in terms of %. When it comes to planning, the main focus now moves from \u201cwhen will it be done?\u201d to \u201chow much risk are you willing to take?\u201d.<\/p>\n

These four myths often hold teams back from embracing probabilistic forecasting, yet the evidence shows it is the most reliable way to make delivery commitments based on real data. I hope these explanations give you the clarity and confidence to apply this approach in your own workflow.<\/p>\n

If you have questions or want to share your experience, feel free to add them in the comments. I will address each one so we can continue building a shared understanding of how to plan with accuracy and confidence. See you next week!<\/p>\n

<\/div>\n
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A lot of teams, however, still question whether they should use this method.\u00a0Today, we\u2019ll dispel four of the most widely spread myths of probabilistic forecasting, which all-too-often prevent teams from embracing the advantages of the most […]<\/p>\n","protected":false},"author":1,"featured_media":7249,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"yasr_overall_rating":0,"yasr_post_is_review":"","yasr_auto_insert_disabled":"","yasr_review_type":"","footnotes":""},"categories":[7,70],"tags":[],"class_list":["post-3160","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-project-management","category-team-performance"],"acf":[],"yoast_head":"\nThe Top 4 Myths of Probabilistic Forecasting | Nave<\/title>\n<meta name=\"description\" content=\"Today we\u2019ll dispel four of the most widely spread myths of probabilistic forecasting, 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