What's going on all my healthcare brothers and sisters? I hope that you are having a wonderful day. We are finishing off the ATIT's version 7 science portion of the exam and we are going to be discussing scientific reasoning.
Let's get started. So as always we're going to begin by looking at the objectives that are going to be on this particular portion of the exam. The test outline is going to have a total of nine items that are scored out of the 44 total items for science.
And they are going to include topics that cover basic scientific measurements and measurement tools, applying logic and evidence to a scientific explanation, predicting relationships among events, objects, and processes, and applying the scientific method to interpret scientific investigation. So we're going to start off with the very basic of the basic when it comes to scientific measurements and measurement tools. And that's understanding units of measurement. Scientists utilize the metric system to measure and record their findings.
What you are going to find is we use this a lot in healthcare. We don't use things such as pounds and ounces. We use words like kilograms and milliliters. We primarily use the metric system. So this is really a great opportunity for you to learn the foundations of what it's going to be like once you enter the healthcare field.
Measurements we use for length and distance includes meters. Mass is grams. and volume is liters.
Something else we use a lot is dimensional analysis, and that's used to convert one unit of measurement to another. It's often done by using a conversion factor, which is a ratio that compares two different units of measurement. So for example, if you want to convert from meters to centimeters, you would use the following conversion. Length in centimeters is equal to length in meters times 100. Another example is if you want to convert liters to milliliters.
you would use the following factor. Volume in milliliters is equal to volume in liters times a thousand. So I did a video on this a while ago in regards to metric conversions. I highly, highly, highly recommend that you look that up.
So that way you understand why we're using 100 versus a thousand to really gain knowledge as to what the actual metric system looks like if you don't have a whole lot of experience with it. So now that we understand the basic forms of measurements, scientists really have to select the appropriate measurement tool in order to figure out the best possible measurement depending on what we're measuring. So for example, if we need to measure length of an object, we could use a ruler, a meter stick, or even a tape measure. Length is the distance from one end to another end of an object. If we need to measure mass of an object, then we could use something such as a balance.
Mass is measured by the amount of matter that is found within that object. And then lastly, if we need to measure volume of a liquid, then we can use a graduated cylinder or a biometric pipette like the person behind me is using. Volume is measured by the amount of space that is taken up by that object.
It's really important to choose the right... form of measurement tool for your job because you if you're using the wrong tool you're most likely going to end up with inaccurate results in addition to measurement tools we have to use appropriate scales of measurement for example if the mass of an object is very large then we're most likely going to be measuring it with kilograms versus grams to be more efficient but if the object measured is really small such as like a dust particle It might be necessary to use milligrams or micrograms to measure that particular mass. The same is true for length and distance.
If the object being measured is very large, such as a building, it might be necessary to use kilometers or miles. But if the object being measured is very small, such as a tiny, tiny little molecule, it might be necessary to use nanometers or picometers. Again, it's important to use the appropriate scale of measurement to ensure that you have an accurate result. As with anything in science, we need to use logic and evidence to understand scientific explanations.
We do this by drawing conclusions and using empirical evidence. Empirical evidence is information that is gathered through observations and experimentation. This type of evidence can either be qualitative, which means it can be described in terms of quality and characteristics, or it's going to be quantitative, which means it's described by terms of amount and quantity.
In order to have confidence in the data that scientists collect, experiments need to be repeated with the exact same variables and procedures as performed previously. When the results of these experiments are consistent with each other, they are known to be reproducible. If the results of an experiment cannot be reproduced, it indicates that there was something wrong with that experiment and it's going to need to be repeated again. So for example, let's consider that a scientist is trying to determine the effect of a new drug on patients with a certain type of disease.
The scientist gives the drug to a control group of patients and observes them over a period of time. The scientist then compares the results of those patients who did not receive the drug to those who did receive the drug. If the results of this experiment cannot be reproduced, it means that the drug might not be effective. Therefore, the scientists would need to repeat the experiment with a new group of patients to see if the results are reproducible. If the results are not reproducible, that might indicate that the drug is not effective and probably shouldn't be used.
In order to make confident conclusions about the results of an experiment, scientists must use empirical evidence. So let's talk a little bit about cause and effect relationships. A cause is something that produces an effect, and an effect is the result of the cause.
In order for scientists to identify cause and effect relationships, they have to use empirical evidence. Consider the previous example of the scientist trying to determine if the effect of a new drug on patients with a certain disease is good. The scientists would need to be able to identify if the effect of the medication is working on that specific disease. Now let's take a closer look at what scientists use in evaluating evidence. When scientists are evaluating evidence, they are looking at whether or not the evidence is reliable and it is valid.
Reliable evidence is information that can be trusted and is consistent every time. Valid evidence is information that accurately represents what it is supposed to represent. In order to determine if the evidence is reliable and valid, scientists must use that process of analyzing data that is free from any biases. They avoid this by experimenting with placebo groups. A placebo group is a harmless substance that is given to patients that have absolutely no therapeutic effect.
By using placebo groups, scientists can be sure that any effects seen are due to the drug and not due to any other factor. Scientists also rely on independent and controlled variables. An independent variable is a variable that is being tested and is not affected by any other variable.
A controlled variable is a variable that is not being tested and is held constant. So, for example, a scientist might be testing the effect of a new drug on patients with a certain disease. The independent variable would be the new drug and the controlled variable would be the new drug.
would be the diseases of those patients. By keeping the controlled variable constant, scientists can be sure that any changes that are actually seen with the experiment are due to that independent variable. So one of my favorite things to talk about is predicting relationships among events, objects, and processes. And we start by looking at comparing magnitude. So when scientists are trying to determine the relationship among events, objects, and processes, They often use evidence from experiments to make those predictions.
In order to make those predictions, scientists must be able to compare the magnitude or the size of that evidence. So for example, the diameter of a human hair can be measured in micrometers, whereas the height of a human can be measured in meters. It's important to understand the concept of scale when you're comparing magnitude of evidence. Next, let's look at how we're going to determine casual relationships in sequences of events.
Casual relationships are difficult to determine. As we know, a cause is something that produces an effect, and an effect is a result of that cause. In order for scientists to identify a cause and effect relationship, they must use empirical evidence between two variables.
Examples can be high blood pressure and vascular disease. We know they affect each other. But how do they affect each other?
What evidence do we have? Determining a casual relationship can involve identifying the sequence of events that leads to a consequence. So again, for example, a sequence of events may include the process of body temperature increasing and the breakdown of glycogen in the liver. What does that sequence of events look like?
The sequence of events would be as follows. First, the body temperature rises, and then second, the brain alerts the sweat glands to begin sweating. That would be a sequence of events.
Hopefully that cleared up kind of how we determine those casual relationships when it comes to events. Next, we're getting into scientific investigations, and we begin by identifying a relevant hypothesis based on the given investigation that we are doing. The scientific method is the process.
that scientists use to answer questions about the world around them. The steps in the scientific method are as follows. First, we identify a problem or question.
What needs to be fixed? Second, we gather information about that problem or that question. Third, we form a hypothesis which is a possible answer for that problem or question.
Fourth, we design and conduct an experiment that tests that hypothesis. Fifth, we analyze the data from that experiment and draw our conclusions. And then lastly, we communicate the results of the experiment.
The scientific method is an important tool that scientists use to interpret scientific investigations. A relevant hypothesis is a hypothesis that is based on the information given in that investigation. So for example, If a scientist is investigating the effect of a new drug on patients with a certain disease, the relevant hypothesis would be that the new drug will cure that disease, right?
That's the whole point of making that medication. Another example of a hypothesis may be that a new diet will actually help patients lose weight. So a scientific hypothesis is a prediction that may occur during an experiment based on previously gathered background research.
The experiment must be conducted to identify if the hypothesis is accurate and valid. We talked about those before. So for example, any experiment can be conducted to identify certain things. We could look at the effect of sugary drinks and their relationship on obesity.
The independent variable would be the sugary drinks, and the dependent variable would be the obesity. The control group would be given a carbonated type of water. to drink whereas the experimental group would be given those sugary drinks.
Both groups will be monitored to see if there is a difference in weight gain. The results of the experiment would help to validate and invalidate the hypothesis. A simple experimental design to test a hypothesis should include an independent variable, a dependent variable, and a control group.
Another important aspect may be to consider the size of each experimental group. If the sample size is too small, the results of the experiment may not be accurate. It's really important to carefully design an experiment in order to understand if the results are going to be accurate and reliable. So how do we identify our dependent variables, independent variables, and experimental controls? A dependent variable is a variable that is being measured in that experiment.
The independent variable is the variable that is being manipulated in that experiment. And the experimental controls are the conditions that are always kept the same with the experiment. So for example, if a scientist is investigating eight hours of sleep, helping people become more alert or not, the dependent variable would be the alertness. The independent variable would be sleep.
And the experimental control would be to keep all conditions the same, whether it's the same room, the same lighting, the same noise level. And lastly, we need to determine whether experimental results or modules support or contradict the hypothesis, prediction, and conclusion. So as we know, for example, if the hypothesis in a new drug is to help cure it, the results of that experiment has to either support or contradict this hypothesis of being true or false.
If the experimental result shows that the new drug does indeed cure that disease, then the hypothesis is absolutely supported. However, if the experimental result shows the new drug does not cure that disease, then the hypothesis is obviously false. It's really important that scientists gather as much data and experimentation observations as possible in order to make these either supports or contradictions to their hypothesis. In some cases though, they might end up with information that is inconclusive, meaning that the experiment is not very clear, and there's a lot more research that needs to be done in order for it to be made clear. Inconclusive results are often extremely frustrating for scientists, but they can provide an opportunity for further research to make sure that whatever you're getting is going to either help you or not help you at all.
I hope that this information was helpful in understanding the ATIT's scientific reasoning of the test. If you have any additional questions, make sure that you leave them down below. I love answering your questions. Head over to my website at www.nursechung.com where there is a lot of additional information that is available to you. And as always, I will see you in the next video.
Bye!