When people think of controlled experiments, they tend to think of testing hypotheses. But what exactly are hypotheses and why do controlled experiments need them? In this blog post, we’ll explore the importance of scientific hypotheses in a controlled experiment and explain why they’re so essential. Experiments are one of the most reliable methods to test if a certain variable affects another variable. To be able to test this reliably, it is important to state your assumptions in advance (also called a null hypothesis), which we call your “hypotheses”. These scientific statements are used as the basis for which you can then decide with statistical significance if there’s any effect or not. Let’s see what this means and how it works in practice.

**Why Are Hypotheses Important To Controlled Experiments?**

**Hypotheses are important to controlled experiments because they define the outcome that researchers are trying to achieve. A hypothesis is a prediction of what the researchers expect to find based on their understanding of the situation. It helps researchers focus their efforts on areas that are likely to provide useful results. Hypotheses also allow researchers to assess the quality of their data and the results of their experiments.**

**When Are Hypotheses Important In An Experiment?**

**1. To state your assumptions**

Before you can start a controlled experiment, it is essential to state your hypotheses. These hypotheses should be written down in advance and kept in mind before running the experiment. In this way, you can make sure that your experiment will be able to prove or disprove these hypotheses, depending on which type of hypothesis you have. For example, if you run an experiment to test whether a certain drug works as advertised by the manufacturer, then you hypothesize that the drug does work. However, if you’re testing if there’s a difference between two drugs and one of them doesn’t work at all, then you hypothesize that there is no difference between the drugs (meaning that neither of them works). For more examples and information on types of hypotheses see our scientific statement article.

**2. To interpret results with statistical significance**

After completing an experiment and analyzing the results using a statistical test (for example ANOVA or t-test), you can tell whether or not there was any effect on your dependent variable (the variable that depends on another variable) caused by one or more independent variables (the variables that cause changes in another variable). But how do we know if this is her variable)? If you found a statistically significant difference between the means of your groups, then this means that there was a real effect on your dependent variable and you can state with statistical significance that your hypothesis is proven. For example, if you experimented to test whether a certain drug works, then you would hypothesize that the drug does work. If a posthoc test (like Tukey’s HSD) shows however that there was no significant difference between the mean of the treated group and the mean of the placebo group (= p > 0.05), then this means that it is unlikely to say with statistical significance that your hypothesis was proven. In other words, there was no effect on your dependent variable.

**3. To choose which variables to control**

Another advantage of stating hypotheses in advance is that they can also be used to determine which variables you will control in an experiment. For example, in an experiment where we want to find out whether or not there’s a difference between two drugs A and B, we may decide beforehand that we will not control for gender or age because we want to see whether there’s any effect on these variables anyway. In this case, our hypothesis would her variable, like the effect of an advertising campaign on the sales of a product). For example, if your experiment showed that the average sales for your product increased by 10% after you ran your advertising campaign, but you have no idea if this was due to chance or not. A statistical test helps evaluate whether or not there was a real effect. If it was statistically significant, then it’s likely that there was an effect and your hypothesis was proven right. However, if it wasn’t statistically significant, then there might have been no real effect at all and you will have to repeat the experiment until you can confirm or deny your hypothesis.

**4. To understand why results were what they were**

After having run many experiments in which you tested different variables and their effects on each other using statistics such as ANOVA, t-test, etc., you will be able to see what results are expected under certain conditions (for example in which situations does a certain variable produce an increase in sales for instance). This can help you predict what results to expect in future experiments and help explain why things happen as they do in nature too. For example, if a certain drug is known to cause more side effects when taken with certain variables. This is called the statistical significance of your results. If there was no effect, then the result is statistically significant and you can reject your null hypothesis (the hypothesis that there was no effect). On the other hand, if there was an effect, then the result is not statistically significant and you can’t reject your null hypothesis.

**Benefits Of Using Hypotheses In Your Experiment**

**1. To understand why these hypotheses are important**

After completing an experiment, it’s important to write down your hypotheses and interpret them with statistical significance so that you can understand why they’re important. For example, in our example, above we mentioned that if you want to test if a certain drug works as advertised by its manufacturer, then you hypothesize that it does work (or does not work). But this hypothesis becomes even more interesting when you consider other hypotheses such as: “If a certain drug doesn’t variable). This is the meaning of statistical significance (which we explain below) and it is important to know how you can interpret the results. For example, if there was no effect on the dependent variable, then your result would be statistically insignificant. If there was an effect on your dependent variable that was statistically significant, then you could say with a high enough probability that this effect is not just a coincidence. It means that a different cause or explanation could be responsible for the result. In such cases, you should either keep doing more experiments or run further experiments with different experimental conditions to get more data points with which you can prove whether or not there’s an effect on your dependent variable.

**2. To compare results from one experiment to another**

In many science cases, we need several experiments before we can draw any conclusion about our hypothesis (such as in drug trials). So it’s essential to compare the results of these different experiments with each other and see the variable). This is why it’s essential to state your hypotheses before running the experiment. If there wasn’t any effect and you didn’t state your null hypothesis, then it would be impossible for you to tell if your dependent variable had any effect.

**3. To show that a certain hypothesis is false**

When testing a specific hypothesis, it is important to make sure that no other possible explanation exists for the results of the experiment (or at least not in a way that could lead to your conclusion being false). In this way, you can disprove the null hypothesis (that there was no difference between the two groups) and show that this theory is incorrect. For example, if you hypothesized that a certain drug does work and in an experiment, there was no difference between two groups of people receiving this drug or a placebo pill, then this null hypothesis is disproved: There was no difference between two groups receiving this drug or a placebo pill.

**4. To make sure your experiment is valid**

It’s also important to check whether or not your experiment has been conducted correctly. A big part of this is ensuring that all of the variables in the experiment have been controlled (for example make sure that all of the conditions are identical). It also means that you should ensure that all of the data in your experiment has been collected correctly (for example make sure that you’ve gathered data from all participants). To learn more about how to do this and how to avoid errors in experiments, see our scientific statement article.

**Key Takeaway**

The main takeaway from this article is that when it comes to controlled experiments, it is important that all the hypotheses are stated in advance and that all of them are tested. It is also important to understand that there are two hypotheses: the null hypothesis and the alternative hypothesis. The key is to find the right hypothesis for your experiment and experiment before you start designing anything.

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