A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light bulb, experimenting with thousands of materials before succeeding.

An algorithm is a step-by-step problem-solving formula that virtually guarantees a solution. Think of it as a recipe with detailed instructions that produce the same result every time. Algorithms are integral to everyday life, particularly in computer science. Search engines like Google use algorithms to rank search results, and social media platforms like Facebook use them to display posts on your newsfeed. Although algorithms ensure a solution, they can be laborious. For example, solving a Rubik's Cube involves a specific sequence of moves that must be followed to achieve the correct configuration.

Comparing these strategies, trial, and error is effective but time-consuming, suitable when there are fewer possible solutions to test. On the contrary, an algorithm is systematic and guaranteed to work but can be complex and lengthy. Both strategies have their applications depending on the nature of the problem and the desired efficiency.