Q: A generalizing hypothesis describes a pattern you think may exist between two variables: an independent variable and a dependent variable. If your experiments confirm the pattern, you may decide to suggest a reason that the pattern exists or a mechanism that generates the pattern. The reason or mechanism you suggest is an explanatory hypothesis.  You can think of the independent variable as the one that is causing some kind of difference or effect to occur. In the examples, the independent variable would be biological sex, i.e. whether a person is male or female, and fertilizer type, i.e. whether the fertilizer is organic or non-organically-based. The dependent variable is what is affected by (i.e. "depends" on) the independent variable. In the examples above, the dependent variable would be the measured impact of caffeine or fertilizer. Your hypothesis should only suggest one relationship. Most importantly, it should only have one independent variable. If you have more than one, you won't be able to determine which one is actually the source of any effects you might observe. Once you've spent some time thinking about your research question and variables, write down your initial idea about how the variables might be related as a simple declarative statement.  Don't worry too much at this point about being precise or detailed. In the examples above, one hypothesis would make a statement about whether a person's biological sex might impact the way the person is affected by caffeine; for example, at this point, your hypothesis might simply be: "a person's biological sex is related to how caffeine affects his or her heart rate." The other hypothesis would make a general statement about plant growth and fertilizer; for example your simple explanatory hypothesis might be "plants given different types of fertilizer are different sizes because they grow at different rates." Hypotheses can either be directional or non-directional. A non-directional hypothesis simply says that one variable affects the other in some way, but does not say specifically in what way. A directional hypothesis provides more information about the nature (or "direction") of the relationship, stating specifically how one variable affects the other.  Using our example, our non-directional hypotheses would be "there is a relationship between a person's biological sex and how much caffeine increases the person's heart rate," and "there is a relationship between fertilizer type and the speed at which plants grow." Directional predictions using the same example hypotheses above would be : "Females will experience a greater increase in heart rate after consuming caffeine than will males," and "plants fertilized with non-organic fertilizer will grow faster than those fertilized with organic fertilizer." Indeed, these predictions and the hypotheses that allow for them are very different kinds of statements. More on this distinction below. If the literature provides any basis for making a directional prediction, it is better to do so, because it provides more information. Especially in the physical sciences, non-directional predictions are often seen as inadequate. Once you have an initial idea on paper, it's time to start refining. Make your hypotheses as specific as you can, so it's clear exactly what ideas you will be testing and make your predictions specific and measurable so that they provide evidence of a relationship between the variables. Where necessary, specify the population (i.e. the people or things) about which you hope to uncover new knowledge. For example, if you were only interested the effects of caffeine on elderly people, your prediction might read: "Females over the age of 65 will experience a greater increase in heart rate than will males of the same age." If you were interested only in how fertilizer affects tomato plants, your prediction might read: "Tomato plants treated with non-organic fertilizer will grow faster in the first three months than will tomato plants treated with organic fertilizer." Your hypothesis must suggest a relationship between two variables or a reason that two variables are related that can feasibly be observed and measured in the real and observable world. For example, you would not want to make the hypothesis: "red is the prettiest color."  This statement is an opinion and it cannot be tested with an experiment. However, proposing the generalizing hypothesis that red is the most popular color is testable with a simple random survey. If you do indeed confirm that red is the most popular color, your next step may be to ask: Why is red the most popular color? The answer you propose is your explanatory hypothesis. Often, hypotheses are stated in the form of if-then sentences. For example, "if children are given caffeine, then their heart rates will increase." This statement is not a hypothesis. This kind of statement is a brief description of an experimental method followed by a prediction and is the most common way that hypotheses are misrepresented in science education.   An easy way to get to the hypothesis for this method and prediction is to ask yourself why you think heart rates will increase if children are given caffeine. Your explanatory hypothesis in this case may be that caffeine is a stimulant. At this point, some scientists write a research hypothesis, a statement that includes the hypothesis, the experiment, and the prediction all in one statement. For example, If caffeine is a stimulant, and some children are given a drink with caffeine while others are given a drink without caffeine, then the heart rates of those children given a caffeinated drink will increase more than the heart rate of children given a non-caffeinated drink. It may sound strange, but researchers rarely ever prove that a hypothesis is right or wrong. Instead, they look for evidence that the opposite of their hypotheses is probably not true. If the opposite (caffeine is not a stimulant) is probably not true, the hypothesis (caffeine is a stimulant) probably is true.  Using the above example, if you were to test the effects of caffeine on the heart rates of children, evidence that your hypothesis is not true, sometimes called the null hypothesis, could occur if the heart rates of both the children given the caffeinated drink and the children given the non-caffeinated drink (called the placebo control) did not change, or lowered or raised with the same magnitude, if there was no difference between the two groups of children. It is important to note here that the null hypothesis actually becomes much more useful when researchers test the significance of their results with statistics. When statistics are used on the results of an experiment, a researcher is testing the idea of the null statistical hypothesis. For example, that there is no relationship between two variables or that there is no difference between two groups. Make your observations or conduct your experiment. Your evidence may allow you to reject your null hypotheses, thus lending support to your experimental hypothesis. However, your evidence may not allow you to reject your null hypothesis and this is okay. Any result is important, even when your result sends you back to the drawing board. Constantly having to go "back to the drawing board" and refine your ideas is how authentic science really works!
A: Determine your variables. Generate a simple hypothesis. Decide on direction. Get specific. Make sure it is testable. Write a research hypothesis. Contextualize your hypothesis. Test your hypothesis.

Q: Many air purifiers use non-washable HEPA filters. Turn off and unplug your appliance before accessing the filter. Check your product manual for specific instructions on how to access your appliance’s filter. At least one foam or activated charcoal filter usually accompanies a non-washable HEPA filter. These accompanying filters typically need to be rinsed for two to three minutes, or until water runs clear. Towel dry your foam or activated charcoal filters, then let them air dry completely for at least 24 hours. Use your vacuum cleaner’s hose with a nozzle or brush attachment to clean your non-washable HEPA filter. Run the attachment over the filter until you’ve removed all debris. Take care not to puncture the filter with the vacuum attachment. Reassemble your appliance after the washable filters have dried. You can wrap the HEPA filter tightly in plastic while you’re waiting for the other filters to dry or during any other extended period of non-use. Some air purifiers have an electronic filter clean reminder. If yours has one, reset it after cleaning your filter.
A:
Remove the filter from your appliance. Wash your appliance’s other filters. Run a vacuum cleaner hose attachment over the filter. Reassemble the appliance.