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ST314 Introduction to Biostatistics Assignment Sample NUI Galway Ireland

ST314 Introduction to Biostatistics course will provide students with statistical methods and tools for the analysis of biomedical data. Biostatistics is the application of statistical methods to biological data and is a vital component of modern biomedical research. This course will cover basic concepts and methods in biostatistics, including exploratory data analysis, estimation, hypothesis testing, and regression.

The course will also cover more advanced topics such as survival analysis and longitudinal data analysis. Throughout the course, students will learn to use statistical software to perform data analysis.

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In this section, we are describing some assigned activities. These are:

Assignment Activity 1: Understand the key concept of variability.

In statistics, variability is a measure of how to spread out data points in a data set. It can be measured in terms of the range, variance, and standard deviation.

  • The range is the difference between the highest and lowest values in a data set.
  • The variance is a measure of how far each data point is from the mean.
  • The standard deviation is a measure of how spread out data points are from the mean.

Variability is important because it allows us to see how different data sets are from each other. It also allows us to compare data sets that have different means and standard deviations. For example, if we want to compare the heights of two groups of people, we can use the standard deviation to see how different they are.

There are many sources of variability, but some common ones are measurement error, sampling error, and variation in the population. Measurement error is an error that occurs when data are measured. A sampling error is an error that occurs when a sample is taken from a population. Variation in the population is a variation that exists within a population.

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Assignment Activity 2: Understand the ideas of population, sample, parameter, statistic, and probability.

A population is a group of individuals, objects, or items from which a sample can be drawn. A sample is a subset of the population that is used to represent the population. A parameter is a characteristic of a population. A statistic is a characteristic of a sample. Probability is the chance that an event will occur.

  • Populations can be infinite or finite. Finite populations are those that have a definite number of members, while infinite populations are those that do not have a definite number of members. For example, the population of all people in the world is infinite, while the population of all people in a particular country is finite.
  • Samples can also be either infinite or finite. A finite sample is a subset of a population that contains a definite number of members, while an infinite sample is a subset of a population that does not have a definite number of members.
  • Parameters are characteristics of populations. They can be descriptive or inferential. Descriptive parameters summarize the data in a population, while inferential parameters make predictions about a population.
  • Statistics are characteristics of samples. They can also be descriptive or inferential. Descriptive statistics summarize the data in a sample, while inferential statistics make predictions about a population.
  • Probability is the chance that an event will occur. It is usually expressed as a number between 0 and 1.

Assignment Activity 3: Understand simple ideas of point estimation.

In statistics, point estimation is the process of estimating a population parameter using a single value from a sample. The single value is called a point estimate.

There are two types of point estimates: unbiased and biased.

  • Unbiased point estimates are those that have a mean that is equal to the population parameter.
  • Biased point estimates are those that have a mean that is not equal to the population parameter.

There are many methods of point estimation, but some common ones are the method of moments, the maximum likelihood method, and the least-squares method.

  • The method of moments is a method of estimating a population parameter using the first moment (the mean) of a sample.
  • The maximum likelihood method is a method of estimating a population parameter using the value of the sample that is most likely to occur.
  • The least-squares method is a method of estimating a population parameter by minimizing the sum of the squares of the errors.

Point estimation is important because it allows us to estimate population parameters without having to collect all of the data from the population. This can be helpful when the population is large or when it is expensive or difficult to collect all of the data.

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Assignment Activity 4: Recognise the additional benefits of calculating interval estimates for unknown parameters and be able to interpret interval estimates correctly.

Interval estimation is a method of estimating population parameters using a range of values. The range of values is called an interval estimate.

Interval estimation is important because it allows us to estimate population parameters with more precision than point estimation. It also allows us to quantify the uncertainty of our estimates.

There are two types of interval estimates: confidence intervals and prediction intervals.

  1. Confidence intervals are used to estimate population parameters. They give us a range of values that is likely to contain the population parameter. The level of confidence is the probability that the population parameter will be contained in the interval.
  2. Prediction intervals are used to predict future values. They give us a range of values that is likely to contain the future value. The level of confidence is the probability that the future value will be contained in the interval.

When interpreting interval estimates, it is important to remember that they are not exact. There is always a chance that the population parameter will be outside of the interval.

It is also important to remember that the level of confidence is not the same as the probability that the population parameter will be contained in the interval. The level of confidence is the probability that the interval will contain the population parameter.

Assignment Activity 5: Carry out a variety of commonly used hypothesis tests.

A hypothesis test is a statistical test that is used to determine whether or not a hypothesis is true.

There are two types of hypothesis tests: null hypothesis tests and alternative hypothesis tests.

  • Null hypothesis tests are used to determine if the null hypothesis can be rejected. The null hypothesis is the hypothesis that there is no difference between the population parameters.
  • Alternative hypothesis tests are used to determine if the alternative hypothesis can be accepted. The alternative hypothesis is the hypothesis that there is a difference between the population parameters.

There are many different types of hypothesis tests, but some common ones are the t-test, the chi-squared test, and the F-test. 

  • The t-test is a hypothesis test that is used to compare two means.
  • The chi-squared test is a hypothesis test that is used to compare two proportions.
  • The F-test is a hypothesis test that is used to compare two variances.

When carrying out hypothesis tests, it is important to remember that there is always a chance of making a Type I error. A Type I error is when the null hypothesis is rejected when it is true.

It is also important to remember that there is always a chance of making a Type II error. A Type II error is when the null hypothesis is not rejected when it is false.

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Assignment Activity 6: Understand the difference between paired and independent data and be able to recognize both in practice.

Paired data are data that are collected on two or more subjects who are matched in some way.

Independent data are data that are collected on two or more subjects who are not matched in any way.

Paired data are often used when there is a need to control for confounding variables. Independent data are often used when there is no need to control for confounding variables.

Paired data are often used in clinical trials. Independent data are often used in observational studies.

Assignment Activity 7: Understand the aims and desirable features of a designed experiment.

A designed experiment is an experiment that is carried out to test a specific hypothesis.

There are many different types of designed experiments, but some common ones are randomized controlled trials, cohort studies, and case-control studies.

  • Randomized controlled trials are experiments that compare two or more groups of subjects who are randomly assigned to each group.
  • Cohort studies are experiments that follow a group of subjects over time.
  • Case-control studies are experiments that compare two or more groups of subjects who have different outcomes.

Many different features make up a well-designed experiment, but some common ones are sample size, randomization, blinding, and replication.

  • The sample size is the number of subjects that are included in the experiment.
  • Randomization is the process of assigning subjects to groups in a random way.
  • Blinding is the process of keeping subjects unaware of which group they are in.
  • Replication is the process of repeating the experiment to confirm the results.

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Assignment Activity 8: Calculate the sample size needed for one and two-sample problems.

Sample size refers to the number of individuals in a study. To calculate the sample size needed for one and two sample problems, several things need to be considered, such as the population size, the desired level of precision, and the desired level of significance.

  • The first step is to determine the population size. The population size is the number of individuals that make up the group that you are interested in. For example, if you were interested in the number of people who have a specific disease, the population size would be the total number of people in the world.
  • The next step is to determine the desired level of precision. The desired level of precision is the amount of error that you are willing to accept in your results. For example, if you wanted your results to be within plus or minus 5% of the true value, then your desired level of precision would be 5%.
  • The last step is to determine the desired level of significance. The desired level of significance is the probability of getting a result that is due to chance. For example, if you wanted your results to be 95% significant, then your desired level of significance would be 5%.

Once you have determined the population size, the desired level of precision, and the desired level of significance, you can use a sample size calculator to determine the number of individuals that you need to include in your study.

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