MCAT Physics and Math Review

Chapter 11: Reasoning About the Design and Execution of Research

11.2 Basic Science Research

Basic science research—the kind conducted in a laboratory, and not on people—is generally the easiest to design because the experimenter has the most control. Often a causal relationship is being examined because the hypothesis generally states a condition and an outcome. In order to make generalizations about our experiments, we must make sure that the outcome of interest would not have occurred without our intervention, and therefore, we use controls. We must also demonstrate causality, which is relatively simple in basic science research, but less so in other research areas.


In basic science research, conditions can be applied to multiple trials of the same experiment that are as near to identical as possible. In this way, a control or standard acts as a method of verifying results. Consider the following experiment: a scientist has an unknown concentration of a basic ammonia solution, but wishes to determine the concentration experimentally. He takes a standardized solution of hydrochloric acid (made by comparison to a potassium hydrogen phthalate [KHP] standard) and titrates the basic solution in the presence of the same calibrated pH meter he used for the hydrochloric acid standardization. He then determines the ammonia concentration from the results of the titration. Because the concentration of the acid used to determine the ammonia concentration was verified against a standard, he can be confident that the calculated ammonia concentration is accurate.


The use of controls also allows investigators to check for contamination of reagents.

Controls can also be separate experimental conditions altogether. For example, when testing the reaction of a tissue culture to an antibiotic, a separate culture is generally grown and administered an equal quantity of a compound known to be inert, like water or saline. The control corrects for any impact that the simple addition of volume might have had on the experiment. Some experiments have both positive and negative controls for points of comparison or a group of controls that can be used to create a curve of known values. Positive controls are those that ensure a change in the dependent variable when it is expected. In the development of a new assay for detection of HIV, for example, administering the test to a group of blood samples known to contain the HIV virus could constitute a positive control. Negative controls, in contrast, ensure no change in the dependent variable when no change is expected. With the same assay, administering the test to a group of samples known not to contain the HIV virus could constitute a negative control. In drug trials, a negative control group is often used to assess for the placebo effect—an observed or reported change when an individual is given a sugar pill or sham intervention.


The other big advantage to being able to manipulate all of the relevant experimental conditions is that basic science researchers can often establish causality. Causality is an if–then relationship, and is often the hypothesis being tested. In basic science research, we manipulate an independent variable, and measure or observe a dependent variable. Where there is a theoretical or known mechanism that links the independent and dependent variables, a causal relationship can be investigated. If the change in the independent variable always precedes the change in the dependent variable, and the change in the dependent variable does not occur in the absence of the experimental intervention, the relationship is said to be causal.


The independent variable is the one that the experimenter is manipulating, and the dependent or outcome variable is the one that is being observed. On a graph the independent variable belongs on the x-axis and the dependent variable belongs on the y-axis.


Establishing causality requires an if–then relationship, which is the cornerstone of formal logic. Formal logic is discussed in Chapter 6 of MCAT Critical Analysis and Reasoning Skills Review.


In basic science research, experimental bias is usually minimal. The most likely way for an experimenter’s personal opinions to be incorporated is through the generation of a faulty hypothesis from incomplete early data and resource collection. However, there can be manipulation of the results by eliminating trials without appropriate background, or by failing to publish works that contradict the experimenter’s own hypothesis.

The low levels of bias introduced by the experimenter do not eliminate all error from basic science research. Measurements are especially important in the laboratory sciences, and the instruments may give faulty readings. Instrument error may affect accuracy, precision, or both. Accuracy, also called validity, is the ability of an instrument to measure a true value. For example, an accurate scale should register a 170-pound person’s weight as 170 pounds. Precision, also called reliability, is the ability of the instrument to read consistently, or within a narrow range. The same person standing on a scale that is accurate but imprecise may get readings between 150 and 190 lbs. The same person standing on a scale that is inaccurate but precise may get readings between 129 and 131 pounds, a relatively narrow range. Accuracy and precision are represented in Figure 11.1. Because bias is a systematic error in data, only an inaccurate tool will introduce bias, but an imprecise tool will still introduce error.

Figure 11.1. Accuracy (Validity) and Precision (Reliability) of Measurements

MCAT Concept Check 11.2:

Before you move on, assess your understanding of the material with these questions.

1.    An experiment with an improperly tared (zeroed) mass balance would suffer from what type of error?

2.    Label the axes and provide representative data for the following situation: An experimenter adds sodium hydroxide to an experimental solution and records the pH. He finds that the relationship is sigmoidal and that the pH is rising.

3.    What is the purpose of a control during experiments? What characteristic of experimental research would be reduced in the absence of a control?