The scientific method refers to a group of techniques that are used to investigate phenomena and acquire and integrate new knowledge or revise existing bodies of knowledge through the process of making observations and conducting experiments (Nuzzo, 2014). The method can be applied in everyday life by observing a particular issue and recording the observations. The recorded data can then be used to conduct an experiment that will involve the testing of a hypothesis. The results of the experiment are then analyzed, and a conclusion is drawn.

Today, life is virtually characterized by people wanting to get the best and most efficient results out of whatever process they involve themselves. For instance, people will always want to use the best and most cost efficient route whenever they commute from one point to the next. However, it is problematic to determine the route that would be most cost efficient, given that there are a number of factors to be considered. For example, the condition of the road, the distance covered, traffic intensity and even the safety of the road (in terms of the probability of being car jacked).

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**"Applications of the Scientific Method".**

The scientific method can be used to determine the most efficient route by conducting an experiment through the use of a testable hypothesis. The hypothesis, in this case, would be:

Based on this hypothesis, the most cost efficient route would be one that boasts the shortest distance from one point to the next. It is imperative to note that the shorter the distance the less the fuel consumed and time wasted; which result in cost efficiency. For this reason, the hypothesis suits the given problem. In the conduction of the experiment, the paper will attempt to assess all factors involved in cost efficiency during travel. The outcomes of the experiment will most likely be that there are many other factors (besides distance, fuel consumption and time taken) that determine cost efficiency. Logically, a shorter route would result in low fuel consumption, but if the road is in poor condition, the damage to the car will result in high costs for the car owner. As such, there are a few other factors that will be required to determine cost efficiency.

The process of testing the hypothesis will be dependent on the type and number of variables that are involved in this particular problem. Here, there are two types of variables; the dependent variable (cost efficiency) and independent variables (route distance, condition of road, fuel consumption, traffic intensity, safety of road, and time taken). As a result, the hypothesis will be tested using the multiple regression model so that the effect of each of the mentioned independent variables on the dependent variable will be assessed (Lewis, 2007). Below is the model that will be used to test the hypothesis:

Where

Notably, the coefficients of the independent variables will be either positive or negative values depending on the effect of the independent variable on the dependent one. For instance, a longer distance would imply a higher cost; therefore, the coefficient for route distance will be a positive value. On the other hand, a road in good condition results in low costs. Therefore, road condition will have a negative coefficient.

The success of the program will be evaluated using the Analysis of Variance (ANOVA) table, so that the values for the sum of squares (SST and SSE) and those for the Mean Squares (MSE and MST) will be used to analyze the truth or falsity of the hypothesis. The null hypothesis will be considered true if the ratio between the MSE and MST equals 1. In other words, if the Mean Square for Error and Mean Square for Treatments are equal, the hypothesis will be true. If the MST is larger or greater than the MSE, the hypothesis will be false.

The strategy used to test the hypothesis (regression model and ANOVA table) facilitates the attainment of reflective results because it factors in all possibilities; including the error. It is notable that the model assesses the effect of each independent variable on the dependent one by allocating each respective variable a coefficient that is indicative of its effect. The ANOVA table facilitates the drawing of conclusions because it enables the experiment to compare the mean squares for the purposes of establishing the truth behind the hypothesis.

The most probable result of the experiment is that the hypothesis will be rejected. In the event of this, the additional steps that might be taken would be to revise the hypothesis so that it factors in all variables that determine cost efficiency during travel. For instance, the hypothesis could be revised to state that the most cost efficient route would be one that has the shortest distance, best road condition, low traffic intensity, and high security. Time and fuel consumption are dependent on the stated factors; therefore, they will not be included in the revised hypothesis.

- Lewis, S. (2007). Regression analysis. Practical neurology, 7(4), 259-264.
- Nuzzo, R. (2014). Scientific method: statistical errors. Nature, 506(7487), 1-19.