The alternative hypothesis is the complete opposite of the null hypothesis. It states that there is something going on, there is a significant difference between the mean or the proportion of our sample and the population. A number of statistical tests have been developed in the literature in order to make a decision regarding the homogeneity of a set of sites composing a region. In this regard, the L-moment based test proposed by Hosking and Wallis is one of the most used and well-known, denoted as HW test in the following (Hosking and Wallis, 1997). Second, the final fit models uncover some interesting patterns of the relation between built form and pedestrian activities.

what is statistical testing

This confirms that the built form has a strong influence on the distribution of pedestrian movements. Nevertheless, considering the strong correlations between individual measures and pedestrian movements and the aforementioned complementarities between different measures, these strong model predictions are not, in fact, that surprising. Statistics are the arrangement of statistical tests which analysts use to make inference from the data given. These tests enables us to make decisions on the basis of observed pattern from data. Other statistical tests are available, but they all compare within-group variance (how to spread out the data inside a category) against between-group variance (how different the categories are from one another). If the between-group variation is big enough that there is little or no overlap between groups, your statistical test will display a low p-value to represent this.

Spatial Mapping and Environmental Risk Identification

Type II error will be the case where the teacher passes the student [do not reject H0] although the student did not score the passing marks [H1 is true]. Type I error will be the teacher failing the student [rejects H0] although the student scored the passing marks [H0 was true]. The crucial point in this situation is that the alternate hypothesis (H1), not the null hypothesis, decides whether you get a right-tailed test. The so-called parametric tests can be used if the endpoint is normally distributed. The group comparison for two categorical endpoints is illustrated here with the simplest case of a 2 × 2 table (four-field table) [Figure 1]. However, the procedure is similar for the group comparison of categorical endpoints with multiple values [Table 1].

  • As you can see, the resulting p-Value is very small in comparison with our significance level.
  • Right-sided hypothesis can be used when we want to know if the mean or proportion of the population is larger than our sample data.
  • Indeed, considering d independent univariate tests, each of which is at the 5% significance level, then (1−0.95d) is the probability of getting at least one significant result, which may be unacceptably large.
  • From these tables, it is easily determined that, for example, 68.26% of the area under the curve lies within ±1 standard deviation and that 95.46% lies within ±2 standard deviations.
  • This form of theory appraisal is the most heavily criticized application of hypothesis testing.
  • It was adequate for classwork and for operational use, but it was deficient for reporting results.

Before selecting a statistical test, a researcher has to simply answer the following six questions, which will lead to correct choice of test. The selection of the statistical test before the study begins ensures that the study results do not influence the test selection. The null hypothesis is, “there is no difference between the active treatment and the placebo with respect to antihypertensive activity”.

As you can see from the result of code snippets above, the resulting p-Value is very small. Hence even if we set the significance level to 0.01, our data provides very strong evidence that the mean salary of male graduates is indeed higher than the mean salary of female graduates. Most interesting are the distinct predictors of pedestrian activities between main road and alleyway segments. For the main road network, no measures of immediate environment are shown to be significant in the models for pedestrian movements, suggesting that street network and land use layout play a defining role in affecting movement patterns. Of the two significant accessibility indices, Gravity Accessibility to retail services is shown to be clearly dominant (its t value is considerably higher than that of Spatial Integration).

The statistical test produces a number called p-value (that is also bounded between 0 and 1). The p-value is the probability of obtaining the data or more extreme data under the null hypothesis. In two tails, the test sample is checked to be greater or less than a range of values in a Two-Tailed test, implying that the critical distribution area is two-sided.

Limitations of Hypothesis Testing

In this use case, we’re going to use one variable from the dataset, which is religion. One variable contains the salary of male graduates and another contains the salary of female graduates. To properly conduct this test, we need to make sure that our data fulfills the following conditions. If z value is less than critical value accept null hypothesis else reject null hypothesis. The choice between one-tailed and two-tailed tests depends on the specific research question and the directionality of the expected effect. If you are interested in statistics of data science and skills needed for such a career, you ought to explore Simplilearn’s Post Graduate Program in Data Science.

In other words, if the researcher makes a mistake in calculation, then the statistical tests will conclude that a false drug sample is a correct drug sample. Further, the researcher might end up tagging a false drug sample as a correct drug sample. Thus, the researcher should be cautious while performing statistical tests. In the field of medicine and nursing, errors in statistical tests can result in huge problems in people’s lives, as it affects their drugs and dosages etc. Hypothesis testing relies exclusively on data and doesn’t provide a comprehensive understanding of the subject being studied. Additionally, the accuracy of the results depends on the quality of the available data and the statistical methods used.

what is statistical testing

Hypothesis testing is used to assess the plausibility of a hypothesis by using sample data. Such data may come from a larger population, or from a data-generating process. The word “population” will be used for both of these cases in the following descriptions. Paired T-Test-Tests for the difference between two variables from the same population( pre- and post test score). For example- In a training program performance score of the trainee before and after completion of the program.


Statistical tests are carried out extensively in psychology, medicine, nursing and business. So far, we have covered the case where we want to infer one variable. In some cases, what we want to do instead is to compare two independent variables and observe whether there is any significant difference between two variables. As you can see, the p-Value for this case is 0.722, which means that it is higher than our significance level.

static testing definition

We use indepence test when we want to observe whether there is an association between two discrete or categorical variables. Notice that because of the way we formulate the alternative hypothesis, this means that we conduct a right-sided hypothesis. With paired t-tests, the goal is different compared to one-sample t-test. Instead of comparing our sample with the population, we want to compare two different conditions on the same variable and then check whether there is any significant difference between the two conditions.

Percent of the area under the standard normal curve as a function of z-score. Statistical Testing is a testing method whose objective is to work out the undependable software package products instead of discovering errors. Check cases are designed for applied mathematics testing with a wholly different objective than those of typical testing. The null hypothesis is a statement about a population parameter, such as the population mean, that is assumed to be true.

what is statistical testing

The significance of location shift in the mean of a population in a series is tested using Mann-Whitney’s (MW) test (Yue and Wang, 2002). Moreover, a single extremely high hydrological value results from different natural and artificial factors that will affect the series’ distribution and pattern. Such events can occur due to the modification of instrumentation or climate change.

Test selection for group comparison with two categorical endpoints. They are shown the back face of a randomly chosen playing card 25 times and asked which of the four suits it belongs to. Not rejecting the null hypothesis does not mean the null hypothesis is “accepted” (see the Interpretation section).

If a normally distributed continuous parameter is compared in more than two paired groups, methods based on ANOVA are also suitable. The factor describes the paired groups—e.g., more than two points of measurement in the use of a therapy. With an unpaired or independent study design, results for each patient are only available under a single set of conditions.