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100 word responses to discussions 400 words totalCameron Izzi

WednesdayOct 7 at 5:44pm

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Statistical testing is extremely important definitely when trying to validate an experiment or gain insight in a dataset. It is equal important to know when to use the appropriate test to validate or back claim one is trying to make. If someone is trying to claim that two data points (or sets) are statistical relevant to each other due to their means being similar, they would not choose to use an f-test. A t-test would be more useful to back their claim in this situation. The f-test would help someone claiming the difference or bias in a datasets are similar because they have the same variance.
It is important to perform statistical testing on the data because it can also give you insight in the data so you are not holding one piece of data above another for unwariness reasons. This can happen a lot when someone feels like the know something about the dataset because they have past experience in the field, some odd years ago. Data changes all the time and their assumptions of that might not be valid today, so statistical testing have help them weight out the important features of that data to help get an unbiased view of the dataset. This very important to make sure future experiments are not be weighted by a human bias in the data, and the appropriate testing, values, features, and data engineering is being performed correctly. This case is very important when dealing with data engineering for training machine learning models, as it makes sure that human bias is not injected into the training process; thus in-turn making the model bias by human nature. 

Jamal Thomas

FridayOct 9 at 7:15am

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Statistical testing sets the groundwork for statistics by allowing researchers to make inferences because they can show whether an observed pattern is due to intervention or chance (“Types of Statistical Tests”, n.d.). Without statistical testing, there is no way to determine if patterns are intentional or by happenstance. Also, because there are so many different types of statistical tests, there are different ways to adapt your test to your study. Researchers need statistical tests to study and comprehend their data, as well as test their hypotheses. Different types of statistical tests include chi-square tests, ANOVA tests, paired and independent T-tests, simple and multiple regression tests, as well as several others.
 Steffond Johnson

ThursdayOct 8 at 11:01am

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A cross-sectional descriptive study was conducted among 470 parents visiting the Department of Pediatrics, Rabindranath Tagore Medical College and Hospital. A 32 item questionnaire covering socio-demographic characteristics and questions pertaining to KAP regarding IOH care was used to collect the data. Descriptive statistics, Student’s t-test, one-way analysis of variance, and Scheffe’s test were used for the statistical analysis (P ≤ 0.05). Majority of the parents had good knowled

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