Page 2 - Scientific Inquiry and Reasoning (question type) Study Guide for the MCAT

Reason about the Research Process

This type of MCAT question will test what you know about the process of science and research. What makes research sound and valid? What specific examples can you give to support a given answer? You need to understand the roles of these components of scientific study:

Theory, Observation, and Past Findings

A scientific theory is a well-supported explanation for an aspect of the natural world. Theories are supported by observations and past findings, and all the current data cannot disprove them. Theories predict and explain new observations. You will need to be able to distinguish between theory, observation, and past findings.

A Testable Research Question or Hypothesis

A hypothesis is a testable statement and typically includes a predicted observation. For example, you may think that UV light is important for vitamin D production. Your hypothesis would be that if UV light is important for vitamin D production (testable statement), then people exposed to lots of sunlight will have high amounts of vitamin D and people exposed to low levels of sunlight will have low amounts of vitamin D (prediction).

Samples and Populations

Sample and population are 2 types of data sets. A sample is all of the data pertaining to an individual from the population. A population is the pooled data from all of the individual samples.

Variables

An independent variable in an experiment changes in response to a regulated factor (e.g. what is manipulated in the experiment) and ideal experiments have only 1 independent variable. A dependent variable changes in response to an independent variable (e.g., what you are measuring). A confounding variable is something that can alter the dependent variable. Controls are important to understand the influence of confounding variables.

Qualities of Tools Used in Natural Sciences

Many of the natural sciences, such as biology, physics, and chemistry rely on methods that obtain quantifiable or measurable data. The relationships among the measured factors are used to create the laws of nature. In contrast, social sciences, such as psychology and sociology, may use more qualitative assessments in their experiments.

Qualities of Tools Used in Behavioral and Social Sciences

Behavioral sciences (e.g., psychology and cognitive science) investigate human and animal interactions with others using observation. Social sciences (e.g., sociology, public health, anthropology) study the effect of social organizations on individuals or groups. Behavioral and social sciences can use methods that collect quantitative and qualitative data.

Variables and Causal Relationships in Research Studies

Scientists often alter one variable (independent) and measure the changes that occur to a different variable (dependent), trying to demonstrate a causal relationship. Causality is the relationship between cause and effect, and temporality occurs when the effect occurs after the cause, even though there may be a delay between cause and effect. When studying humans and animals whose genotypes or living conditions cannot be controlled, subjects are randomly assigned into groups for the research study with the hope that the groups will be representative and equivalent.

Ethical Issues in Science

There are many ethical issues in science. Some of these issues are human cloning, animal experimentation, human experimentation (e.g., paid trials in third world countries that would not be approved in first world countries), genetic testing of embryos for disease, and who profits from research discoveries. Scientists and the countries that fund them have different ethical standards, which can potentially lead to dissent.

Read and Interpret Data

In this type of question, you will be required to read, comprehend, and give the meaning of data obtained from research. What does it all mean? You will need to use tables, graphs, and charts to formulate statements about scientific results. Items may ask you to answer these types of questions and perform these types of tasks:

What Does this Data Mean?

After an experiment is completed, conclusions are made that try to explain the data. Conclusions must be created carefully. Conclusions do not extrapolate and only describe the data in the experiment.

Does the Data Make Sense?

First, identify the hypothesis. Does the data support the hypothesis? If not, what is an alternative explanation of the data?

It is also important to evaluate the experiment when considering the data. Was the correct technique selected for the experiment? Were the correct variables tested? Should different controls be used? If the answer to any of these questions is no, then the data cannot be interpreted.

How Can Scientific Measures Help Interpret this Data?

Statistics help to assess the robustness of the data. The central tendency is the typical value for a probability distribution. Examples include the mode, the mean, and the median. In contrast, measures of dispersion describe how scattered the data is. Examples of dispersion include standard deviation, variance, and interquartile range.

Consider Random and Systematic Error

Random errors are introduced into the experiment by unknown factors or changes. Random changes can occur in the environment or the instrument (e.g., random change in current that alters an instrument’s measuring capability) and may not affect all the samples in an experiment. Systematic error is typically introduced when using the measuring apparatus and will affect all the samples (e.g., the thermometer is not calibrated or user error).

What is the Statistical Significance of the Data?

Statistical significance (p) assesses the probability that the results of an experiment are due to chance instead of the variables being tested. Typically, if p < 0.05, then the results are not likely due to mere chance. The p value should be selected prior to the experiment. Confidence intervals are used to assess variation in the population and are calculated using the experimental data.

How are the Variables Related?

A variable is something that can change. In an ideal experiment, the independent variable is altered in a controlled manner, which results in changes in the dependent variable that is being measured. For example, you could test the hypothesis that sunlight regulates plant growth. You would alter the amount of sunlight (independent variable) that plants receive and measure their growth (dependent variable).

Can You Make Predictions Based on the Data?

During this test, data may be presented in the text, figures, tables, etc. You will have to examine the data and use it to answer questions. For example, you may see a graph showing the effects of a treatment on several age groups. The treatment is effective in adults (20–40 years of age), but not in children under 10. You may be asked to predict how this treatment would affect teenagers (ages 11 to 20), which is not shown on the graph.

Can You Draw Conclusions and Answer Research Questions Based on the Data?

Data will be presented and you will have to apply it to a problem. For example, “Drug Y” lowers glucose in the blood rapidly in healthy adults. Patients with diabetes suffer many detrimental effects due to high blood glucose levels. You might be asked if “Drug Y“ should be used to treat patients with diabetes.

Is It Possible to Identify Real World Implications of the Data?

Research and novel discoveries can be applied to solve everyday problems. For example, when assessing a novel drug as a potential new therapeutic to treat a disease, the data regarding this drug must be critically examined. Is the drug safe? Is the dosage realistic? Is the current treatment more effective? If the answer to any of these questions is no, then a new solution is needed.

The Math Component

Performing well on the MCAT will involve a certain amount of math knowledge and skill as it applies to scientific data. There is no calculus required, however, and you will have access to a copy of the periodic table.

These are some of the things you should review:

Significant Digits

Significant digits are the digits that are meaningful to the measurement. Significant digits become important when raw data is used for calculations. For example, a standard curve is generated using the experimental data obtained with samples of known concentrations, and the instrument reports data in 0.1 intervals. Using the standard curve, the value for an unknown sample is calculated to be 2.548. The sample’s value should be 2.5 to reflect the sensitivity of the instrument.

Reasonable Numerical Estimates

Reasonable numerical estimates are approximations. You should be able to perform these estimations quickly. For example, the reasonable approximation of 278 x 19 is 600, because 278 is rounded up to 300 and 19 is rounded up to 20. 300 x 20 is 600.

Metric Unit Conversion

The metric system is an internationally agreed-upon measuring system for times, volumes, distances, temperatures, etc. You may have to convert metric units; however, the conversion factors will be provided.

Dimensional Analysis

Dimensional analysis determines the relationship between different physical quantities and is useful for converting physical quantities. This technique is also called the unit-factor method and the factor-label method. An example of a dimensional analysis problem would be to determine the number of moles in so many grams of carbon.

Square Root Estimation

There are several methods of square root estimation. The simplest is to remember the perfect square roots and to use them as guides. For example, what is the square root of 52? The square root of 49 is 7. Because 49 is a bit smaller than 52, you know that the square root of 52 will be slightly larger than 7.

Scientific Notation

Scientific notation is a method used to express very large or very small numbers (e.g., 6 x 1023). Typically, positive and negative exponents indicate large or small values, respectively.

Here are some additional math topics you will want to review in preparation for this type of question:

• Linear, semilog, and log-log scales

• Calculating slope from graphic data

• Probability

• Ratio and Proportion

• Exponentials and logarithms, Algebra II level

• Simultaneous equations

• Concepts in trigonometry:
  • basic functions: sine cosine, tangent
  • inverse functions: sin-\(1\), cos-\(1\), tan-\(1\)
  • sin and cosine values: \(0^\circ, 90^\circ\), and \(180^\circ\)
  • side length rules for right triangles with angles of \(30^\circ, 60^\circ\), and \(90^\circ\)

• Vector addition and subtraction

• The right-hand rule

Note: Dot and cross products are not tested.