In the previous Basic Biology blog titled The Scientific Method, we explored the fundamentals of the scientific method, a structured approach that scientists employ to drive discovery and expand our understanding of the natural world. While the method outlines a systematic sequence of steps, in practice, scientific inquiry often deviates from this rigid framework. A notable example arises when researchers embark on experiments only to realize midway that additional foundational information is needed, prompting them to backtrack to gather essential details. Similarly, challenges can arise when analyzing experimental data due to limitations in technology or existing knowledge, necessitating further investigation or technological advancements to decode observations effectively. One illustrative case is the pioneering work on DNA, where elucidating its structure was a prerequisite for unraveling the mechanisms by which genes encode proteins. This dynamic process underscores the fluidity and adaptability inherent in scientific exploration beyond the textbook depiction of the scientific method

To accurately portray the realities of researchers’ work, the model of the scientific process should be adjusted to accommodate unforeseen challenges. Central to this revised model is the formulation and testing of hypotheses, which underpins scientific explanations of natural phenomena. However, it’s crucial to acknowledge that the creation and evaluation of hypotheses are influenced not only by scientists’ interactions within their community and society at large, but also by the exploration process itself. A compelling illustration of these intertwined influences is the scientific community’s dedication to cancer research. Cancer biology, particularly the mechanisms of cancer cell proliferation, has captivated researchers for decades. This collective interest shapes which hypotheses are pursued, how data is interpreted, and the scientific significance of resultant discoveries. When considering societal influence, it’s essential to recognize the profound impact of cancer on individuals and families, motivating widespread societal efforts to combat the disease. This societal imperative further drives researchers to formulate and test hypotheses related to cancer biology.

During an experiment, scientists manipulate one variable, known as a factor, and observe its impact on another factor. This type of experimentation is termed a controlled experiment, which aims to elucidate the differences between an experimental group and a control group. It’s essential to recognize that both the manipulated factor and the observed factor are experimental variables, integral characteristics of any experiment. For example, consider an experiment investigating how sunlight affects plant growth. The amount of sunlight serves as the independent variable, manipulated by researchers. Conversely, the actual growth of the plants constitutes the dependent variable, measured to assess outcomes. Identifying the dependent variable is straightforward, as the dependent variable can be easily identified as it reflects what the experiment aims to evaluate. In our plant growth example, the experiment seeks to understand the impact of sunlight on plant growth, making plant growth “dependent” on sunlight availability. Moreover, every experiment includes an experimental group and a control group. The control group receives a standardized value of the independent variable. In our example, the control group consists of 50 plants exposed to 12 hours of sunlight and 12 hours of darkness daily, replicating typical environmental conditions. In contrast, the experimental group experiences variations in the independent variable. For instance, one experimental group might receive 6 hours of sunlight and 18 hours of darkness, while another might receive no sunlight at all, experiencing 24 hours of darkness. In essence, the experimental group represents the part of the experiment where subjects encounter the altered version of the independent variable, also referred to as the experimental variable, to discern its effects on the dependent variable.

You might ask, why is the control group necessary in the first place? Controlled experiments aim to isolate the effect of one factor on another (within a closed system). The only way to ascertain this is by establishing the baseline effect of the independent variable on the dependent variable under normal conditions. This allows for a comparison between the effects of the modified independent variable and those of the standard independent variable on the dependent variable.

There’s a common misconception that a “controlled experiment” implies scientists have complete control over every aspect of their experiment. However, achieving such absolute control is impractical, even in highly regulated laboratories. Instead, researchers have found that they can manage “unwanted” variables by keeping them constant across both experimental and control groups, thereby neutralizing their influence rather than eliminating them entirely. The most effective approach to variable control in experiments is maintaining identical conditions for both groups, with the sole distinction being the independent variable. In the upcoming Basic Biology blog, we will explore the precise meaning of “theory” in science and highlight its distinct nature compared to a hypothesis or speculation!