Science Terms and Definitions You Should Know

Science Terms and Definitions You Should Know Scientific experiments involve variables, controls, a hypothesis, and a host of other concepts and terms that may be confusing. This is a glossary of important science experiment terms and definitions. Glossary of Science Terms Central Limit Theorem: states that with a large enough sample, the sample mean will be normally distributed. A normally distributed sample mean is necessary to apply the t test, so if you are planning to perform a statistical analysis of experimental data, its important to have a sufficiently large sample. Conclusion: determination of whether the hypothesis should be accepted or rejected. Control Group: test subjects randomly assigned to not receive the experimental treatment. Control Variable: any variable that does not change during an experiment. Also known as constant variable Data:  (singular: datum) facts, numbers, or values obtained in an experiment. Dependent Variable: the variable that responds to the independent variable. The dependent variable is the one being measured in the experiment. Also known as the dependent measure, responding variable double-blind: neither the researcher nor the subject knows whether the subject is receiving the treatment or a placebo. Blinding helps reduce biased results. Empty Control Group: a type of control group which does not receive any treatment, including a placebo. Experimental Group: test subjects randomly assigned to receive the experimental treatment. Extraneous Variable: extra variables (not the independent, dependent, or control variable) that may influence an experiment, but are not accounted for or measured or are beyond control. Examples may include factors you consider unimportant at the time of  an experiment, such as the manufacturer of the glassware in a reaction or the color of paper used to make a paper airplane. Hypothesis: a prediction of whether the independent variable will have an effect on the dependent variable or a prediction of the nature of the effect.   Independence  or  Independently:  means one factor does not exert influence on another. For example, what one study participant does should not influence what another participant does. They make decisions independently. Independence is critical for a meaningful statistical analysis. Independent Random Assignment: randomly selecting whether a test subject will be in a treatment or control group. Independent Variable: the variable that is manipulated or changed by the researcher. Independent Variable Levels: refers to changing the independent variable from one value to another (e.g., different drug doses, different amounts of time). The different values are called levels. Inferential Statistics: applying statistics (math) to infer characteristics of a population based on a representative sample from the population. Internal Validity: an experiment is said to have internal validity if it can accurately determine whether the independent variable produces an effect. Mean: the average calculated by adding up all the scores and then dividing by the number of scores.   Null Hypothesis: the no difference or no effect hypothesis, which predicts the treatment will not have an effect on the subject. The null hypothesis is useful because it is easier to assess with a statistical analysis than other forms of a hypothesis. Null Results (Nonsignificant Results): results that do not disprove the null hypothesis. Null results dont prove the null hypothesis, because the results may have resulted from a lack of power. Some null results are type 2 errors. p 0.05: This is an indication of how often chance alone could account for the effect of the experimental treatment. A value p 0.05 means that 5 times out of a hundred, you could expect this difference between the two groups, purely by chance. Since the chance of the effect occurring by chance is so small, the researcher may conclude the experimental treatment did indeed have an effect. Note other p or probability values are possible. The 0.05 or 5% limit simply is a common benchmark of statistical significance. Placebo (Placebo Treatment):  a  fake treatment that should have no effect, outside of the power of suggestion. Example: In drug trials, test patients may be given a pill containing the drug or a placebo, which resembles the drug (pill, injection, liquid) but doesnt contain the active ingredient. Population: the entire group the researcher is studying. If the researcher cannot gather data from the population, studying large random samples taken from the population may be used to estimate how the population would respond. Power: the ability to observe differences or avoid making Type 2 errors. Random or Randomness: selected or performed without following any pattern or method. To avoid unintentional bias, researchers often use random number generators or flip coins  to make selections. (learn more) Results: the explanation or interpretation of experimental data. Statistical Significance: observation, based on the application of a statistical test, that a relationship probably is not due to pure chance. The probability is stated (e.g., p 0.05) and the results are said to be statistically significant. Simple Experiment: basic experiment designed to assess whether there are a cause and effect relationship or test a prediction. A fundamental simple experiment may have only one test subject, compared with a controlled experiment, which has at least two groups. Single-blind: when either the experimenter or subject is unaware whether the subject is getting the treatment or a placebo. Blinding the researcher helps prevent bias when the results are analyzed. Blinding the subject prevents the participant from having a biased reaction. T-test: common statistical data analysis applied to experimental data to test a hypothesis. The t-test computes the ratio between the difference between the group means and the standard error of the difference (a measure of the likelihood the group means could differ purely by chance). A rule of thumb is that the results are statistically significant if you observe a difference between the values that are three times larger than the standard error of the difference, but its best to look up the ratio required for significance on a t table. Type I Error (Type 1 error): occurs when you reject the null hypothesis, but it was actually true. If you perform the t-test and set p 0.05, there is less than a 5% chance you could make a Type I error by rejecting the hypothesis based on random fluctuations in the data. Type II Error (Type 2 error): occurs when you accept the null hypothesis, but it was actually false. The experimental conditions had an effect, but the researcher failed to find it statistically significant.

Read This Before Applying to an Economics PhD Program

Read This Before Applying to an Economics PhD Program I recently wrote an article about the types of people who shouldnt pursue a Ph.D. in economics. Dont get me wrong, I love economics. Ive spent a majority of my adult life in the pursuit of knowledge in the field studying around the world and even teaching it at the university level. You may love studying economics, too, but a Ph.D. program is an entirely different beast that requires a very specific type of person and student. After my article was published, I received an email from a reader, who just happened to be a potential Ph.D. student.   This readers experience and insights into the economics Ph.D. program application process were so on point that I felt the need to share the insights. For those considering applying to a Ph.D. program in Economics, give this email a read. One Students Experience Applying to an Economics Ph.D. Program Thanks for the graduate school focus in your recent articles.  Three of the challenges you mentioned [in your recent article] really hit home: American students have a comparative disadvantage for selection compared to foreign students.The importance of math cannot be overstated.Reputation is a huge factor, especially that of your undergraduate program. I applied unsuccessfully to Ph.D. programs for two years before conceding that I might not be ready for them. Only one, Vanderbilt, gave me even a wait-list consideration. I was a little embarrassed at being shunned. My mathematics GRE was 780. I had graduated at the top of my class with a 4.0 GPA in my economics major and completed a statistics minor. I had two internships: one in research, one in public policy. And accomplished this all while working 30 hours a week to support me. It was a brutally hard couple of years. The Ph.D. departments I applied to and my undergraduate adviser all pointed out: I attended a small, regional public university, and our professors spent significant time with students to the detriment of their own publishing.Though I took a heavy load of statistics coursework, I only had two terms of calculus.I had never been published; not even in an undergraduate journal.I aimed for highly-ranked schools in the Midwest like Illinois, Indiana, Vanderbilt, Michigan, Wisconsin, Washington University in St. Louis, but neglected schools on the coasts, which might have seen me as a more diverse candidate. I also made what many considered a tactical error: I went to talk with the graduate programs before I applied. I was later told that this is a taboo and seen as schmoozing. I even talked at length with the director of one program. We ended up talking shop for two hours and he invited me to attend presentations and brown bags whenever I was in town. But soon I would learn that he would be ending his tenure to take a position at another college, and would no longer be involved in the approval process for that program. After going through these obstacles, some suggested I prove myself with a Masters Degree in Economics first. I had originally been told that many schools pick top candidates immediately after undergraduate, but this new advice made sense because departments commit considerable resources to their Ph.D. candidates and want to make sure their investment will survive first-year exams. With that path in mind, I found it interesting that so few departments offer a terminal Masters in Economic. Id say about half as many as those that offer only the terminal Ph.D. Fewer still offer an academic Masters - most of these are professional programs. Still, Im glad it gives me a chance to dig deeper into research and see if Im ready for Ph.D. research. My Response   This was such a great letter for many reasons. First, it was genuine. It wasnt a why didnt I get into a Ph.D. program rant, but a personal story told with thoughtful insights. In fact, my experience has been nearly identical, and I would encourage any undergraduate student considering pursuing a Ph.D. in economics to take this readers insights to heart. I, myself,  was in a Masters program (at Queens University in Kingston, Ontario, Canada) before I entered my Ph.D. program. Today, I must admit that I wouldnt have survived three months as a Ph.D. student had I not attempted an MA in Economics first.