Science and Technology and Engineering: Scientific Inquiry and Lab Safety (Review)

Components of Scientific Experimentation
A hypothesis is a tentative supposition about a phenomenon
(or a fact or set of facts) made in order to examine and test its logical or empirical consequences through investigation or methodological experimentation.
A theory is a scientifically proven, general principle offered to explain phenomena. A theory is derived from a hypothesis and verified by experimentation and research.
A scientific law is a generally accepted conclusion about a body of observations to which no exceptions have been found. Scientific laws explain things, but do not describe them.
A control is a normal, unchanged situation used for comparison against experimental data.
Constants are factors in an experiment that remain the same.
Independent variables are factors, traits, or conditions that are changed in an experiment. A good experiment has only one independent variable so that the scientist can track one thing at a time. The independent variable changes from experiment to experiment.
Dependent variables are changes that result from variations in the independent variable.

Science as a Series of Processes
Science is not just the steps of experimentation. While the process of posing a question, forming a hypothesis, testing the hypothesis, recording data, and drawing a conclusion is at the heart of scientific inquiry, there are other processes that are important as well. Once the scientist has completed the testing of a hypothesis and possibly come up with a theory, the scientist should then go through the process of getting feedback from colleagues, publishing an article about the work in a peer-reviewed journal, or otherwise reporting the results to the scientific community, replicating the experiment for verification of results (by the original scientist or others), and developing new questions.
Science is not just a means of satisfying curiosity, but is also a process for developing technology, addressing social issues, building knowledge, and solving everyday problems.

Drawing Conclusions After Experiments
Conclusions are based on data analysis and background research. The scientist has to take a hard look at the results of an experiment and check the accuracy of the data to draw preliminary conclusions. These should be compared to the background research to find out if the preliminary conclusion can be supported by previous research experiments. If the results do not support the hypothesis, or if they are contrary to what the background research predicted, then further research is needed. The focus should be on finding a reason for the different results. Finally, the scientist provides a discussion of findings that includes a summary of the results of the experiment, a statement as to whether the hypothesis was proven or disproven, a statement of the relationship between the independent and dependent variable, a summary and evaluation of the procedures of the experiment (including comments about successes and effectiveness), and suggestions for changes/modifications in procedures for further studies.

Steps of the Scientific Method
The steps of the scientific method are as follows:
1. The scientific method begins with, and absolutely depends upon, the observation of objective, unbiased data. Any prejudice or bias in the observed data nullifies its validity. Thus the basic input from the observations must be rigorously screened to ensure objectivity.
2. The development of a theory or hypothesis based on objective data is the next step in the scientific method. These theories or hypotheses pose logical expectations that the experiment may yield.
3. Construction of a rigorous and valid experimental method designed to test the theories is the next phase of the scientific method. This experimental method must be carefully constructed to give objective, unbiased conclusions based on the hypotheses posited.
4. Careful and statistically correct analysis and evaluation of the experimental results make up the next stage.
5.  Modification and replication of the experiment must then follow to provide a statistically accurate demonstration of the validity of the hypothesis.
Equivalent results must be shown from a number of repetitions of the experiment.

Steps of the Scientific Inquiry
Scientific inquiry is the impetus and catalyst for all scientific research and experimentation. It grows from questions about the observed world and gives us a template with which to apply the scientific method. Steps in scientific inquiry include the following principles:

1. Determination of the content and scope of the questions to be investigated is the first step. These may range from simple to extremely complex questions to be explored by scientists.
2. The design, strategy, and method of the inquiry are then carefully considered, and a model for the inquiry is constructed.
3. The formulation of theories and models based on the careful observation of objective, unbiased data then follows. This formulation is derived from the scope of the scientific inquiry and the questions to be investigated.
4. Analysis of possible alternative conclusions drawn from the models and results of experimentation follows.
5. Postulating a theory or constructing a scientific statement based on conclusions is the next logical step.
6. Defending the scientific statement against alternative hypotheses is a critical function of scientific inquiry.
7. Defense of the theory or conclusion against critical analysis is the final step in the process.

Science
Science is a method of acquiring and obtaining knowledge. It is the process of gaining reliable information about the real world, including the explanation of phenomena. It is the development of a body of knowledge about observable phenomena, using the best capabilities humans have at their disposal. The process of organizing and classifying knowledge, through objective observation and evaluation, is a major goal of science. Science can be considered reliable, but it is not infallible. The limits of human knowledge are constantly growing, often making yesterday's science obsolete and simplistic. Science is thus never fixed; it is always subject to change as new information is gained and synthesized with existing knowledge. Ultimately, science is the sum total of knowledge in any period of time, based on the current abilities of man to understand the world of phenomena, verifiable by observable data.

Limitations of Science
There are clear limits on what science can explain.
The demand for objectivity both strengthens knowledge we gain from scientific experiments and limits what we can explore. Beyond the realm of scientific inquiry are such questions as 'Why does anything exist?' or 'What is the meaning of life?' These are subjective questions that do not lend themselves easily to scientific inquiry. These questions, and others like them, come from within, and their conclusions, not validated by science, shape the very fabric of a society. They attempt to give meaning to what may be viewed as chaos.
Periodically, science will impact these subjective conclusions with new evidence. For example, the theory of evolution is regarded as blasphemy by many religious fundamentalists. These conflicts may cause great upheavals in society, leaving many to wonder how science and religious belief can be reconciled. Ultimately, observation of the external world must stand as the true test of science.

Hypothetico-Deductive Process
The hypothetico-deductive process states that to have an idea and then formulate a hypothesis are essentially creative processes, driven by eons of human experience. Statements about reality are the logical conclusions of observed experience. Such creative propositions are the basis for scientific inquiry. Empirical evidence must then validate these hypotheses.
Creative inspiration and scientific validation are interdependent aspects of scientific inquiry. New observations often are the catalyst for creative new theories. Science thus proceeds through creative thinking and scientific validation. There are criticisms of the hypothetico-deductive process: The problem of deduction points out that the original hypothesis may be proved wrong in the future, thus invalidating all subsequent conclusions. The problem of induction, building a theory from generalizations, is that any generalization may be proved wrong by future objective observations. The hypothetico-deductive process, and its problems, is a fascinating subject to philosophers of science.

Hypothesis
It is important to form a hypothesis in order to make a tentative explanation that accounts for an unbiased observation. To be scientific, the hypothesis must be testable through experimentation. Careful construction of the experiment provides that predictions derived from the hypothesis are valid. The hypothesis must be formulated in a manner designed to provide a framework for evaluating the results of an experiment. In many scientific experiments, a hypothesis is posited in negative terms because scientists may accept logically plausible ideas until they are proven false. It is more difficult to prove that a hypothesis is true because its validity must be proven in all possible situations under endless variable conditions. Scientists tend to construct hypotheses for testing by creating experiments that might prove them false. If they succeed in proving it false, the hypothesis must be modified or discarded.

Basic Science and Applied Science
Basic science and applied science share many attributes but are generally motivated by different influences:
Basic science is spurred on by scientific inquiry, the human need to explain the observed physical world. It may have no specific goal, but is mankind's response to questions that arise from human curiosity and interest. It is usually an attempt to explain the laws of nature by using the scientific method.
Applied science has a specific practical goal or application: It is designed to solve a problem. Industry and government are institutions that use applied science regularly.
Thus, basic and applied science share many qualities, including scientific inquiry and the scientific method. The goals of each can be very different. It should be pointed out that basic science very often provides results that have uses in applied science.

Benefits of Basic Science
Although basic science may have no stated goal or target, it provides many benefits to society:

1. Basic science contributes greatly to human understanding and culture, enriching society in many ways.
2. Basic science has been responsible for major breakthroughs that have great social and economic impact.
3. Basic science provides derivative solutions that can be used in applied science. For example, basic science was critical to the development of the space program, from which countless valuable applications have been derived.
4. Basic science contributes to education and research across the broad spectrum of society.

Measuring, Organizing, and Classifying Data
Measuring data is a crucial part of the scientific process. Measurements are most useful if they are quantified—expressed in numbers. Measuring is the process of determining variables such as time, space, and temperature of objects and processes in precise numbers. The metric system is the universal standard of measurement in science. Data must be organized in a practical, useful manner to be valuable. Scientists use graphs, charts, tables, and other organizational tools to make data more useful. Data must then be classified—grouped into organizational schemes for easy access and use. These schemes attempt to organize the maximum amount of useful data in a format that scientists can use.
Although these steps may be less glamorous than other areas of science, they are essential.

Scientific Laws
Scientific principles must meet a high standard to be classified as laws; scientific laws are identified as follows:

1. Scientific laws must be true, not just probable. A highly probable principle may still be proven false.
2. Laws are statements of patterns found in nature.
3. Laws may have a universal form but be stated conditionally. For example, the statement 'All living human beings breathe,' may be restated 'If a thing is a living human being it must breathe.'
4. Laws refer to a general, not a particular, class.
5. Laws have purely qualitative predicates.
6. Laws must have a formal language of expression to be fully understood. Such a language does not exist now.
7. Laws may be probabilistic. For example, a law may state 'Eight percent of all dogs are terriers.'

Common Criticisms of the Deductive Model
The deductive model of the scientific method has the following criticisms:

1. The deductive model fails to make logical distinctions among explanations, predictions, and descriptions of things to be explained.
2. The deductive model is restrictive: It excludes most scientific examples.
3. The deductive model is too inclusive: It admits data that cannot be explained.
4. The deductive model requires an account of cause, law, and probability, which are basically unsound.
5. The deductive model leaves out the elements of context, judgment, and understanding.

Prediction
These are the two widely accepted definitions of scientific prediction:
1. In the language of science, prediction is stating in advance the outcome from testing a theory or hypothesis in a controlled experiment. Based on objective observation of data, scientists move from observation of facts to a general explanation, or hypothesis, which must be confirmed by testing through experimentation.
2.  Another definition of prediction favored by scientific philosophers is the ability of a hypothesis to lead to deductions of scientific statements that could not be anticipated when the hypothesis was posited. In this sense, prediction means what the scientist can verify from what he or she has deduced from the hypothesis.

Laboratory Safety
The following basic rules should be supplemented by standards appropriate to individual laboratories:

1. Hazardous areas must be identified and warnings posted regarding risks.
2. A hazard containment plan must be in effect and readily available.
3. Safe control of airflow must be maintained at all times.
4. Safe work practices must be taught to all who work in the lab.
5. Proper maintenance of laboratory equipment must be enforced.
6. Safe storage of hazardous material must be implemented.
7. Procedures for safe disposal of hazardous wastes must be followed at all times. An updated emergency procedure manual must be available.
8. A complete emergency first aid kit should be accessible at all times.
9. Regular education on the basics of lab safety should be implemented.

Verification and Confirmation of Data in Valid Science
A critical distinction should be made between confirmation and verification of scientific data. Verification establishes once and for all the truth of the statement. Confirmation is the testing of claims to see how true they are. For a claim to be testable, an experiment must be devised to ensure the validity of the results. A claim can only be confirmed when we know the conditions for verification. A claim confirmation is always relative to the testing procedures. Test results must always be objective and observable. Actually, no factual claim can ever be verified because there is always the possibility of new evidence appearing that proves the claim false. A scientific law must also be confirmed by making predictions based on unbiased,

Scientific Measurement and Unit Systems
The basis of quantitative measurement is the unit system.
In order to properly describe how large, how heavy, or how hot something is, it is necessary to have some reference unit. The most commonly used unit system in the world is the SI, short for the Systeme International d'Unites, although the English system is still in popular use in the United States. In SI, there are base units and derived units. Derived units may be stated as some combination of the base units. The SI base units for length, mass, time, temperature, and electric current are meter (m), kilogram (kg), second (s), kelvin (K), and ampere (A), respectively. Some common derived units are newtons () and pascals (). Most SI units can be scaled up or down by adding a prefix. To alter a unit by , , or , add a prefix of kilo (k), mega (M), or giga (G). For instance, a kilometer (km) is  meters. To change the unit by a factor of , , or , add a prefix of milli (m), micro (μ), or nano (n). For instance, a microsecond (μs) is  seconds. It is important to note that the SI base unit kilogram has already employed a prefix to scale up the smaller unit, gram (g). Order of magnitude is a concept that deals with the comparison between two values. If one value differs from the other by a factor of , it may be said that it differs by n orders of magnitude. Other SI base units include the mole and the candela. Some lesser used prefixes include: hecto (h), ; deca (da), ; deci (d), ; and centi (c), .

Data Collection and Analysis
In order to take data measurements from their experimental setups, scientists will often use measurement devices wired to a computer running data-acquisition software. This allows them to take numerous readings per second if necessary. Once the data is collected, it will often be organized using a spreadsheet, such as Microsoft Excel. This multipurpose program contains several options for displaying the data in graphs or charts, as well as tools for performing statistical analyses or linear regressions. Linear regression is a technique used to determine whether gathered data fits a particular trend or equation. It attempts to fit a common line or curve equation to the data set. A very important concept to understand when taking data is that of significant figures. Significant figures, or significant digits, indicate how precisely a quantity is known. Each non-zero digit is always significant. A zero is significant if either: 1) it is to the right of both a non-zero digit and the decimal point; or 2) it is between two other significant digits. For instance, 1.0230 has five significant figures while
0.0230 has only three because the trailing zero is the only zero to the right of both a non-zero and the decimal. When multiplying (or dividing), the product (quotient) has as many significant figures as the factor (dividend or divisor) with the fewest. When adding (or subtracting), the sum (difference) should be rounded to the highest final decimal place of the involved terms. For example,

rounded to three significant figures is 60.2.

Interpretation and Drawing of Conclusions from Data Presented in Tables, Charts, and Graphs
One of the reasons data are so often plotted on graphs is that this format makes it much easier to see trends and draw conclusions.
For instance, on a simple position vs. time graph, the slope of the curve indicates the object's velocity at each point in time. If the curve becomes level, this means the object is not moving. In a simple velocity vs. time graph, the slope of the curve indicates the object's acceleration at each point in time. Additionally, the area contained under the curve indicates the total distance traveled. For instance, if the object is traveling at a constant 5 m/s for 10 s, the velocity vs. time graph will be a straight line at

between

and
.
The area under this curve is
. Thus, the object traveled 50 meters. Nearly any type of quantity may be graphed, so it is important to always check the axis labels to ensure that you know what the graph represents and what units are being used.

Data Errors and Error Analysis
No measurements are ever 100% accurate. There is always some amount of error regardless of how careful the observer or how good the equipment. The important thing for the scientist to know is how much error is present in a given measurement. Two commonly misunderstood terms regarding error are accuracy and precision. Accuracy is a measure of how close a measurement is to the true value. Precision is a measure of how close repeated measurements are to one another. Error is usually quantified using a confidence interval and an uncertainty value. For instance, if the quantity is measured as

on a 95% confidence interval, this means that there is a 95% chance that the actual value falls between 98 and 102. When looking for the uncertainty in a derived quantity such as density, the errors in the constituent quantities, mass and volume in this case, are propagated to the derived quantity. The percent uncertainty in the density,
, can be found by the equation:

Statistical Precision and Errors
Errors that occur during an experiment can be classified into two categories: random errors and systematic errors. Random errors can result in collected data that is wildly different from the rest of the data, or they may result in data that is indistinguishable from the rest. Random errors are not consistent across the data set. In large data sets, random errors may contribute to the variability of data, but they will not affect the average. Random errors are sometimes referred to as noise. They may be caused by a student's inability to take the same measurement in exactly the same way or by outside factors that are not considered variables, but influence the data. A systematic error will show up consistently across a sample or data set, and may be the result of a flaw in the experimental design. This type of error affects the average, and is also known as bias.
Scientific notation is used because values in science can be very large or very small, which makes them unwieldy. A number in decimal notation is 93,000,000. In scientific notation, it is 9.3 × 10⁷.
The first number, 9.3, is the coefficient. It is always greater than or equal to 1 and less than 10. This number is followed by a multiplication sign.
The base is always 10 in scientific notation. If the number is greater than ten, the exponent is positive. If the number is between zero and one, the exponent is negative. The first digit of the number is followed by a decimal point and then the rest of the number. In this case, the number is 9.3. To get that number, the decimal point was moved seven places from the end of the number, 93,000,000. The number of places, seven, is the exponent.

Statistical Terminology
Mean - The average, found by taking the sum of a set of numbers and dividing by the number of numbers in the set.
Median - The middle number in a set of numbers sorted from least to greatest. If the set has an even number of entries, the median is the average of the two in the middle.
Mode - The value that appears most frequently in a data set. There may be more than one mode. If no value appears more than once, there is no mode.
Range - The difference between the highest and lowest numbers in a data set.
Standard deviation - Measures the dispersion of a data set or how far from the mean a single data point is likely to be.
Regression analysis - A method of analyzing sets of data and sets of variables that involves studying how the typical value of the dependent variable changes when any one of the independent variables is varied and the other independent variables remain fixed.