What are hypotheses? The first step in the scientific method is to create a specific statement that you are trying to prove. These statements are called hypotheses and set the bar for what we are trying to accomplish with our data collection. Let's say that you are a human resource manager for a large firm which has been experiencing a high volume of sexual harassment complaints in the last year. You contract with an outside company to conduct your annual sexual harassment training, and now need to evaluate if the training was effective. Begin with your variable of interest, also known as the independent variable. In this scenario, we are interested in the effectiveness of the sexual harassment training. Now, what are we hoping the sexual harassment training will accomplish? A reduction in the number of sexual harassment complaints would be the goal. The number of sexual harassment complaints is the variable we are measuring, which we hope will be impacted by the independent variable. The variable being measured is known as the dependent variable (DV), as changes in the variable should be "dependent" upon our independent variable (IV). So we now have the three pieces we need for hypothesis creation, our IV, DV, and in this case a direction. Let's take a stab at the hypothesis we are trying to prove: The new sexual harassment training significantly reduced the number of sexual harassment complaints. Note that we have all three components in our hypothesis. The hypothesis we are trying to prove is known as the research or alternative hypothesis. We can either say our hypothesis is true, or that it is not true. The next step is to create our null hypothesis. This hypothesis states that our IV had NO impact upon our DV. By creating our research hypothesis first, it is easy to negate to create our null: The new sexual harassment training did not significantly reduce the number of sexual harassment complaints. Although null hypotheses might have little function in the real world, in the scientific method they are integral. In statistical terms, we always either reject or fail to reject the null hypothesis. If we fail to reject the null hypothesis, this means our IV was ineffective. In this case, if the number of complaints was not significantly reduced, this means our training was not effective. If the null hypothesis is rejected, this means our training was effective in significantly reducing the DV, or number of harassment complaints. Let's take a look at how hypotheses play a role in testing