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Trial and error is the simplest experimental approach. Currently, across many industries, this approach is often coupled with fail fast, fail often concept to promote aggressive experimentation to learn and explore new ways of designing products/processes. Gordon Moore, founder of Intel, noted: “With engineering, I view this year's failure as next year's opportunity to try it again. Failures are not something to be avoided. You want to have them happen as quickly as you can so you can make progress rapidly.” In the trial and error method, a 4 step approach is typically followed, wherein the experts start by first observing a problem, then make assumptions on what could or could not possibly work, devise & deploy a solution based on the assumptions, learn valuable lessons from the outcome of the deployed solution. This approach is repeated again and again, each time with adjustments made to the design based on previous learnings, until a convincing solution is arrived at (or a decision is made to simply stop the experiment based on several other factors). Trial and error has many advantages compared with the other experimental setups: a) it is easy and quick to setup b) it does not need special expertise and can easily be implemented by the specific subject matter experts c) it promotes a culture of learning by doing and observing Trial and error should be the last resort if other approaches cannot be used due to the following disadvantages: a) Not rule based and hence, does not provide a structured thinking on all the factors that could affect the experiment b) Its best suited only for simple problems, whereas typical real world problems have a wide variety of variables as inputs c) It cannot be used to predict an outcome as it does not provide enough clues on why a solution works d) It cannot be used to find the best possible solution or all possible solutions Examples to understand trial and error: Our day Is filled with instances where we continuously experiment with new things. Trial and error seen in nature: A monkey figuring out to open a soda bottle with a bottle opener; Trial and error seen in our everyday life: A person trying to figure out the best transportation to commute to work, a baby trying to stand up and walk; Trial and error seen in industry: Supervised machine learning. One factor at a time: As the name implies, an experimental approach in which the factors are tested one at a time. This approach is also known by various other acronyms such as OVAT, OVaaT (one variable at a time). Suppose we have a process with inputs X1, X2 that are processed to provide an output Y. In OFAT, we keep X2 constant and vary X1 to find the optimum result. Once the optimum output is achieved, X1 is fixed at that point and we then vary X2 to find its optimum value. In this way, one factor or variable (ie)., X1 or X2, is varied at a time till all the input factors are exhausted. The main advantage of OFAT is that it serves as a good alternative to multi factor analysis when collecting data for multi factor is cumbersome or not economically viable. OFAT has the following disadvantages: a) Cannot figure out interaction effects between the variables under analysis. This means, OFAT cannot be used if the input variables to a process interact with each other and the interaction also has an impact on the output. b) In certain cases, depending on the number of variables, can lead to large number of experimental runs with a negative impact on cost and time. If you want to understand adding 2 strawberries or 4 strawberries (factor / variables) to 1 glass of milk, with 2 spoons of sugar, mixed for 3 mins in the same mixer (fixed entities) gives the best strawberry smoothie, we can use OFAT. Multiple Factors at a time: When multiple factors are manipulated to study their effect on the response/output, its called multi factor at a time experimental approach. This solves the major disadvantage with OFAT pertaining to interaction effect as the response pertaining to changes on multiple factors at the same time are observed to find the optimum response. In case of random selection of input variables as followed in trial and error, there is no structure which gives clear understanding if all possible combinations of inputs have been analyzed in an orderly way. Hence, with trial and error, its not possible to quantify if the solution finalized is the best optimum. With multi factor analysis, as the experiment can be designed to include all the interactions that is of interest, probability of achieving the best optimum solution with an orderly experimentation is high. Further, the multi factor analysis also provides key insights on the key factors to be considered, the optimal settings for each input variable to be set that would result in the least variation in the process output. Despite the advantages, the subject matter experts in coordination with the management should determine the right use cases for doing a multi factor analysis by taking the complexity, cost of experimentation, multiple runs, practical feasibility for simulation and time needed. An OEM that manufactures Turbines for power generation can use a multi factor analysis experimental approach to find which operational conditions such as ambient temperature, fuel used, fuel gas temperature, etc will result in the maximum power efficiency.

Experiment in statistical terms will refer to a method of conducting test in order to learn or identify something. It's like performing series or unique composition of input to observe outcome, an expected outcome. Most common example is measuring a cover box that needs to be in exact dimension to carry an expensive machine part. A mechanic will continue to focus on getting most accurate measure and may need some attempts to get a perfect 10. Experiments are essential to decide nature of the relationship between independent and dependent variables. The intent is always to design a process that reduces and restricts the variable impact on output. Trial and Error – Repetitive attempts made until the perfect expected outcome is attained. In simple words, continue to practice till the desired result is obtained. Often you will notice a Cricket Batsman will continue to practice hook shots, practice till the time he is most certain to hit a six with least chances of getting caught. Ravi Shastri derived a unique way of pulling the ball and still keep the ball grounded. He surely used Trial and Error method to formulate the shot. The idea is to keep learning and adapting basis learning to get close to better than last time, eventually get to a solution. Trial and Error method is most applicable where the risk or damage due to failure is less and affordable, where the chances are often and in some cases in abundant. You may want to keep trying drive a motorbike and fall but would not put same efforts for landing an airplane. Effectively used in Lean problem solving, Repairs, tuning an engine and obtaining knowledge. One Factor AT A Time also known as one variable at a time that involves testing of one cause or factor at a time rather having multiple factors contributing to the outcome. In absence of historical trends providing expertise, it is viable to use One Factor and track the consequences. It is a simple way to notify how much has the result changes due to one factor. This test is much more controlled and helps in moving the decision logically and within a defined structure. Obviously because of its nature, one cannot predict the relationship between multiple factors and hence optimizing the solution by getting the perfect combination is missed. Also, OFAT may result in multiple runs and basis causes list. Example studying the impact on the strength of a glass bottle by changing Temperature, later Silicon dioxide composition and later funnel to hold the base. Multiple Factors at a Time also called as Fractional Experiment is a design of experiment where two or more factors are involved, each having a specified contribution (levels) and all possible combinations across different levels generates a model. These experiments allow the study of each factor and also their interactions. These designs are more efficient and informative, low on cost and fast in deriving the optimal combination and have a better coverage when it comes to factors and at their unique combinations. Example, a motor mechanic may want to observe power consumption of two machines running at different speeds, carrying different weights and on different terrains. Trial and Error method for a simple reason of being fast, agile, less complex and investments is widely preferred over other experiments. Example in preparing new Combinations of drugs and medicine and antibiotics that will largely evolve with Learn Apply Learn Method and over time. This is also called Structure Activity relationship where relationship is built with what chemical will work best with antibiotic. Success of medicines in controlling covid was largely run with test and error experiments before Vaccination got evolved. Someone did the experiment with multiple paracetamol combination and eventually concluded DOLO 650 to be the best. Similarly, most of the sports team will use the Trial and Error method over other options, especially when the opponent in new team and what may click and work in one circumstance may vary for the next time, these sequences best fit in Trial and Error Method.