Overview Descriptive Statistics Inferential Statistics Graphical Summary Overview The results of your statistical analyses help you to understand the outcome of your study, e. Statistics are tools of science, not an end unto themselves.
Statistical terms used in research studies: However, for media members and public communicators of many kinds it is imperative to do more than just read study abstracts; understanding the methods and concepts that underpin academic studies is essential to being able to judge the merits of a particular piece of research.
Further, the emerging field of data journalism requires that reporters bring more analytical rigor to the increasingly large amounts of numbers, figures and data they use. Grasping some of the academic theory behind statistics can help ensure that rigor.
But detecting such a relationship is only a first step; the ultimate goal is to determine causation: There is a time-honored phrase to keep in mind: Another key distinction to keep in mind is that studies can either statistical business report observed data descriptive statistics or use observed data to predict what is true of areas beyond the data inferential statistics.
Here are some other basic statistical concepts with which journalism students and working journalists should be familiar: A sample is a portion of an entire population.
Inferential statistics seek to make predictions about a population based on the results observed in a sample of that population.
There are two primary types of population samples: For a random sample, study subjects are chosen completely by chance, while a stratified sample is constructed to reflect the characteristics of the population at large gender, age or ethnicity, for example.
There are a wide range of sampling methods, each with its advantages and disadvantages. Attempting to extend the results of a sample to a population is called generalization.
This can be done only when the sample is truly representative of the entire population. Generalizing results from a sample to the population must take into account sample variation.
Even if the sample selected is completely random, there is still a degree of variance within the population that will require your results from within a sample to include a margin of error. For example, the results of a poll of likely voters could give the margin of error in percentage points: The greater the sample size, the more representative it tends to be of a population as a whole.
Thus the margin of error falls and the confidence level rises. Most studies explore the relationship between two variables — for example, that prenatal exposure to pesticides is associated with lower birthweight.
This is called the alternative hypothesis. Well-designed studies seek to disprove the null hypothesis — in this case, that prenatal pesticide exposure is not associated with lower birthweight. If the p-value is 0. Bias comes in many forms but most common bias is based on the selection of subjects.
For example, if subjects self-select into a sample group, then the results are no longer externally valid, as the type of person who wants to be in a study is not necessarily similar to the population that we are seeking to draw inference about.
When two variables move together, they are said to be correlated. Positive correlation means that as one variable rises or falls, the other does as well — caloric intake and weight, for example.
Negative correlation indicates that two variables move in opposite directions — say, vehicle speed and travel time. Causation is when change in one variable alters another. For example, air temperature and sunlight are correlated when the sun is up, temperatures risebut causation flows in only one direction.
This is also known as cause and effect. The correlation coefficient is a measure of linear association or clustering around a line. While causation is sometimes easy to prove, frequently it can often be difficult because of confounding variables unknown factors that affect the two variables being studied.
Studies require well-designed and executed experiments to ensure that the results are reliable. When causation has been established, the factor that drives change in the above example, sunlight is the independent variable.Working on a Statistics Project?
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