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The overwhelming majority of employment lawsuits get tossed out of court at summary judgment, but for the few that survive it can be an uphill battle to prove a case. Most managers and executives don’t wear their bias on their sleeve and it often falls on the plaintiff to submit a substantial amount of proof to get through summary judgment. Granted, most federal statutes place a heavy burden of proof on the employer, but recent Supreme Court decisions are beginning to shift this further toward the employee. Anyone filing a claim with EEOC or their state’s human rights commission would behoove themselves to learn some elementary statistics and analysis in order to substantiate their claims.

If a case actually survives EEOC investigations and later passes summary judgment motions, statistics can form a bulk of the evidence presented at trial. Most of the data for this is learned through discovery, but there are ways to get around this initially using less powerful statistical tests. If discrimination is really present, some analysis may flesh it out and make for compelling evidence when filing a claim with EEOC or filing your case with your attorney.

As an example, take the case of a private school in South Carolina going through a chapter 11 bankruptcy. The school in question, The Byrnes Schools of Florence, SC, filed a voluntary bankruptcy petition on January 31, 2011. This makes all their documents public record and gives us a nice well of data from which to draw. As part of the Ch. 11, they had to submit payroll records, giving a statistician some powerful data to work with. In this case, a number of statistical tests, from the basic t-test to an ANOVA to a linear regression were used to show a disparity in pay between younger and older teachers. The stats show a clear trend that pay actually decreases with age and experience, the opposite of one would usually expect. While not definitive proof, such an analysis serves as a powerful indicator of bias.

If your employer is not bankrupt and not willing to hand over its hiring/salary data, there are still options available. Statistics is based upon taking a smaller sample of a larger group and trying to learn something about the bigger group. If you know your coworkers’ ages or race or other protected status, you could conduct personal interviews to obtain some preliminary data. Salary information is nice because it’s easy to perform statistical analyses on it. Depending on how big the disparity is, the sample size needed to prove it to a significant standard may be rather small.

Generally, salary data can be some of the most powerful, but evaluations and raises and promotions are also fair game. Simply survey a small group of people, some from your same protected class and others from a general pool and look for a difference. There are many freeware stats programs that can do this by simply plugging in the numbers and running the tests.

Simple analyses like these can be powerful tools in the early stages of a discrimination investigation or lawsuit. While the most common reason for dismissal is failure to state a claim – the plaintiff is not actually a member of a protected group under current law – the next most common is a lack of proof. No employer is going to freely admit discrimination, and rebutting their explanation as pretense can be greatly aided by having some strong numbers on your side. In the end, a few simple statistical tests and some nice graphs may tip the scales and encourage the defendant to settle instead of taking the legal fee plunge and hiring experts to refute your analysis.

The overwhelming majority of employment lawsuits get tossed out of court at summary judgment, but for the few that survive it can be an uphill battle to prove a case. Most managers and executives don’t wear their bias on their sleeve and it often falls on the plaintiff to submit a substantial amount of proof to get through summary judgment. Granted, most federal statutes place a heavy burden of proof on the employer, but recent Supreme Court decisions are beginning to shift this further toward the employee. Anyone filing a claim with EEOC or their state’s human rights commission would behoove themselves to learn some elementary statistics and analysis in order to substantiate their claims.

If a case actually survives EEOC investigations and later passes summary judgment motions, statistics can form a bulk of the evidence presented at trial. Most of the data for this is learned through discovery, but there are ways to get around this initially using less powerful statistical tests. If discrimination is really present, some analysis may flesh it out and make for compelling evidence when filing a claim with EEOC or filing your case with your attorney.

As an example, take the case of a private school in South Carolina going through a chapter 11 bankruptcy. The school in question, The Byrnes Schools of Florence, SC, filed a voluntary bankruptcy petition on January 31, 2011. This makes all their documents public record and gives us a nice well of data from which to draw. As part of the Ch. 11, they had to submit payroll records, giving a statistician some powerful data to work with. In this case, a number of statistical tests, from the basic t-test to an ANOVA to a linear regression were used to show a disparity in pay between younger and older teachers. The stats show a clear trend that pay actually decreases with age and experience, the opposite of one would usually expect. While not definitive proof, such an analysis serves as a powerful indicator of bias.

If your employer is not bankrupt and not willing to hand over its hiring/salary data, there are still options available. Statistics is based upon taking a smaller sample of a larger group and trying to learn something about the bigger group. If you know your coworkers’ ages or race or other protected status, you could conduct personal interviews to obtain some preliminary data. Salary information is nice because it’s easy to perform statistical analyses on it. Depending on how big the disparity is, the sample size needed to prove it to a significant standard may be rather small.

Generally, salary data can be some of the most powerful, but evaluations and raises and promotions are also fair game. Simply survey a small group of people, some from your same protected class and others from a general pool and look for a difference. There are many freeware stats programs that can do this by simply plugging in the numbers and running the tests.

Simple analyses like these can be powerful tools in the early stages of a discrimination investigation or lawsuit. While the most common reason for dismissal is failure to state a claim – the plaintiff is not actually a member of a protected group under current law – the next most common is a lack of proof. No employer is going to freely admit discrimination, and rebutting their explanation as pretense can be greatly aided by having some strong numbers on your side. In the end, a few simple statistical tests and some nice graphs may tip the scales and encourage the defendant to settle instead of taking the legal fee plunge and hiring experts to refute your analysis.

The overwhelming majority of employment lawsuits get tossed out of court at summary judgment, but for the few that survive it can be an uphill battle to prove a case. Most managers and executives don’t wear their bias on their sleeve and it often falls on the plaintiff to submit a substantial amount of proof to get through summary judgment. Granted, most federal statutes place a heavy burden of proof on the employer, but recent Supreme Court decisions are beginning to shift this further toward the employee. Anyone filing a claim with EEOC or their state’s human rights commission would behoove themselves to learn some elementary statistics and analysis in order to substantiate their claims.

If a case actually survives EEOC investigations and later passes summary judgment motions, statistics can form a bulk of the evidence presented at trial. Most of the data for this is learned through discovery, but there are ways to get around this initially using less powerful statistical tests. If discrimination is really present, some analysis may flesh it out and make for compelling evidence when filing a claim with EEOC or filing your case with your attorney.

As an example, take the case of a private school in South Carolina going through a chapter 11 bankruptcy. The school in question, The Byrnes Schools of Florence, SC, filed a voluntary bankruptcy petition on January 31, 2011. This makes all their documents public record and gives us a nice well of data from which to draw. As part of the Ch. 11, they had to submit payroll records, giving a statistician some powerful data to work with. In this case, a number of statistical tests, from the basic t-test to an ANOVA to a linear regression were used to show a disparity in pay between younger and older teachers. The stats show a clear trend that pay actually decreases with age and experience, the opposite of one would usually expect. While not definitive proof, such an analysis serves as a powerful indicator of bias.

If your employer is not bankrupt and not willing to hand over its hiring/salary data, there are still options available. Statistics is based upon taking a smaller sample of a larger group and trying to learn something about the bigger group. If you know your coworkers’ ages or race or other protected status, you could conduct personal interviews to obtain some preliminary data. Salary information is nice because it’s easy to perform statistical analyses on it. Depending on how big the disparity is, the sample size needed to prove it to a significant standard may be rather small.

Generally, salary data can be some of the most powerful, but evaluations and raises and promotions are also fair game. Simply survey a small group of people, some from your same protected class and others from a general pool and look for a difference. There are many freeware stats programs that can do this by simply plugging in the numbers and running the tests.

Simple analyses like these can be powerful tools in the early stages of a discrimination investigation or lawsuit. While the most common reason for dismissal is failure to state a claim – the plaintiff is not actually a member of a protected group under current law – the next most common is a lack of proof. No employer is going to freely admit discrimination, and rebutting their explanation as pretense can be greatly aided by having some strong numbers on your side. In the end, a few simple statistical tests and some nice graphs may tip the scales and encourage the defendant to settle instead of taking the legal fee plunge and hiring experts to refute your analysis.

The overwhelming majority of employment lawsuits get tossed out of court at summary judgment, but for the few that survive it can be an uphill battle to prove a case. Most managers and executives don’t wear their bias on their sleeve and it often falls on the plaintiff to submit a substantial amount of proof to get through summary judgment. Granted, most federal statutes place a heavy burden of proof on the employer, but recent Supreme Court decisions are beginning to shift this further toward the employee. Anyone filing a claim with EEOC or their state’s human rights commission would behoove themselves to learn some elementary statistics and analysis in order to substantiate their claims.

If a case actually survives EEOC investigations and later passes summary judgment motions, statistics can form a bulk of the evidence presented at trial. Most of the data for this is learned through discovery, but there are ways to get around this initially using less powerful statistical tests. If discrimination is really present, some analysis may flesh it out and make for compelling evidence when filing a claim with EEOC or filing your case with your attorney.

As an example, take the case of a private school in South Carolina going through a chapter 11 bankruptcy. The school in question, The Byrnes Schools of Florence, SC, filed a voluntary bankruptcy petition on January 31, 2011. This makes all their documents public record and gives us a nice well of data from which to draw. As part of the Ch. 11, they had to submit payroll records, giving a statistician some powerful data to work with. In this case, a number of statistical tests, from the basic t-test to an ANOVA to a linear regression were used to show a disparity in pay between younger and older teachers. The stats show a clear trend that pay actually decreases with age and experience, the opposite of one would usually expect. While not definitive proof, such an analysis serves as a powerful indicator of bias.

If your employer is not bankrupt and not willing to hand over its hiring/salary data, there are still options available. Statistics is based upon taking a smaller sample of a larger group and trying to learn something about the bigger group. If you know your coworkers’ ages or race or other protected status, you could conduct personal interviews to obtain some preliminary data. Salary information is nice because it’s easy to perform statistical analyses on it. Depending on how big the disparity is, the sample size needed to prove it to a significant standard may be rather small.

Generally, salary data can be some of the most powerful, but evaluations and raises and promotions are also fair game. Simply survey a small group of people, some from your same protected class and others from a general pool and look for a difference. There are many freeware stats programs that can do this by simply plugging in the numbers and running the tests.

Simple analyses like these can be powerful tools in the early stages of a discrimination investigation or lawsuit. While the most common reason for dismissal is failure to state a claim – the plaintiff is not actually a member of a protected group under current law – the next most common is a lack of proof. No employer is going to freely admit discrimination, and rebutting their explanation as pretense can be greatly aided by having some strong numbers on your side. In the end, a few simple statistical tests and some nice graphs may tip the scales and encourage the defendant to settle instead of taking the legal fee plunge and hiring experts to refute your analysis.

The overwhelming majority of employment lawsuits get tossed out of court at summary judgment, but for the few that survive it can be an uphill battle to prove a case. Most managers and executives don’t wear their bias on their sleeve and it often falls on the plaintiff to submit a substantial amount of proof to get through summary judgment. Granted, most federal statutes place a heavy burden of proof on the employer, but recent Supreme Court decisions are beginning to shift this further toward the employee. Anyone filing a claim with EEOC or their state’s human rights commission would behoove themselves to learn some elementary statistics and analysis in order to substantiate their claims.

If a case actually survives EEOC investigations and later passes summary judgment motions, statistics can form a bulk of the evidence presented at trial. Most of the data for this is learned through discovery, but there are ways to get around this initially using less powerful statistical tests. If discrimination is really present, some analysis may flesh it out and make for compelling evidence when filing a claim with EEOC or filing your case with your attorney.

As an example, take the case of a private school in South Carolina going through a chapter 11 bankruptcy. The school in question, The Byrnes Schools of Florence, SC, filed a voluntary bankruptcy petition on January 31, 2011. This makes all their documents public record and gives us a nice well of data from which to draw. As part of the Ch. 11, they had to submit payroll records, giving a statistician some powerful data to work with. In this case, a number of statistical tests, from the basic t-test to an ANOVA to a linear regression were used to show a disparity in pay between younger and older teachers. The stats show a clear trend that pay actually decreases with age and experience, the opposite of one would usually expect. While not definitive proof, such an analysis serves as a powerful indicator of bias.

If your employer is not bankrupt and not willing to hand over its hiring/salary data, there are still options available. Statistics is based upon taking a smaller sample of a larger group and trying to learn something about the bigger group. If you know your coworkers’ ages or race or other protected status, you could conduct personal interviews to obtain some preliminary data. Salary information is nice because it’s easy to perform statistical analyses on it. Depending on how big the disparity is, the sample size needed to prove it to a significant standard may be rather small.

Generally, salary data can be some of the most powerful, but evaluations and raises and promotions are also fair game. Simply survey a small group of people, some from your same protected class and others from a general pool and look for a difference. There are many freeware stats programs that can do this by simply plugging in the numbers and running the tests.

Simple analyses like these can be powerful tools in the early stages of a discrimination investigation or lawsuit. While the most common reason for dismissal is failure to state a claim – the plaintiff is not actually a member of a protected group under current law – the next most common is a lack of proof. No employer is going to freely admit discrimination, and rebutting their explanation as pretense can be greatly aided by having some strong numbers on your side. In the end, a few simple statistical tests and some nice graphs may tip the scales and encourage the defendant to settle instead of taking the legal fee plunge and hiring experts to refute your analysis.

The overwhelming majority of employment lawsuits get tossed out of court at summary judgment, but for the few that survive it can be an uphill battle to prove a case. Most managers and executives don’t wear their bias on their sleeve and it often falls on the plaintiff to submit a substantial amount of proof to get through summary judgment. Granted, most federal statutes place a heavy burden of proof on the employer, but recent Supreme Court decisions are beginning to shift this further toward the employee. Anyone filing a claim with EEOC or their state’s human rights commission would behoove themselves to learn some elementary statistics and analysis in order to substantiate their claims.

If a case actually survives EEOC investigations and later passes summary judgment motions, statistics can form a bulk of the evidence presented at trial. Most of the data for this is learned through discovery, but there are ways to get around this initially using less powerful statistical tests. If discrimination is really present, some analysis may flesh it out and make for compelling evidence when filing a claim with EEOC or filing your case with your attorney.

As an example, take the case of a private school in South Carolina going through a chapter 11 bankruptcy. The school in question, The Byrnes Schools of Florence, SC, filed a voluntary bankruptcy petition on January 31, 2011. This makes all their documents public record and gives us a nice well of data from which to draw. As part of the Ch. 11, they had to submit payroll records, giving a statistician some powerful data to work with. In this case, a number of statistical tests, from the basic t-test to an ANOVA to a linear regression were used to show a disparity in pay between younger and older teachers. The stats show a clear trend that pay actually decreases with age and experience, the opposite of one would usually expect. While not definitive proof, such an analysis serves as a powerful indicator of bias.

If your employer is not bankrupt and not willing to hand over its hiring/salary data, there are still options available. Statistics is based upon taking a smaller sample of a larger group and trying to learn something about the bigger group. If you know your coworkers’ ages or race or other protected status, you could conduct personal interviews to obtain some preliminary data. Salary information is nice because it’s easy to perform statistical analyses on it. Depending on how big the disparity is, the sample size needed to prove it to a significant standard may be rather small.

Generally, salary data can be some of the most powerful, but evaluations and raises and promotions are also fair game. Simply survey a small group of people, some from your same protected class and others from a general pool and look for a difference. There are many freeware stats programs that can do this by simply plugging in the numbers and running the tests.

Simple analyses like these can be powerful tools in the early stages of a discrimination investigation or lawsuit. While the most common reason for dismissal is failure to state a claim – the plaintiff is not actually a member of a protected group under current law – the next most common is a lack of proof. No employer is going to freely admit discrimination, and rebutting their explanation as pretense can be greatly aided by having some strong numbers on your side. In the end, a few simple statistical tests and some nice graphs may tip the scales and encourage the defendant to settle instead of taking the legal fee plunge and hiring experts to refute your analysis.

It is your employer’s duty of care and responsibility to ensure that the workplace is a safe environment to work in. Your employers are also responsible for any visitors to their premises such as customers, suppliers and the general public.

Accidents are at work are very common and can occur in any job, in any situation. Your employer legally has to comply with the Health and Safety regulations which have been enforced the workplace is a safe environment. If they do not fulfil this requirement, they are breaking the law.

Providing the employer was responsible for the accident at work, you should seek the legal advice from a personal injury solicitor. You may be entitled to compensation.

Here’s why:

One:

If the injury or injuries you have sustained have prevented you from being able to work, a personal injury solicitor will access your claim and advice whether or not you may be eligible to claim for any financial loss you have suffered.

Two:

You might have suffered a permanent injury, condition or disfigurement, and so your day-to-day lifestyle has been affected. Your personal injury solicitor will take this into account and ensure that you are compensated accordingly.

Three:

Severe injuries can be life changing and in some cases can lead to psychological damage If you have suffered a psychological injury, you may also be entitled to claim compensation, dependent on the severity and impact it has on your day-to-day lifestyle.

Four:

You might have suffered damage to your personal belongings as part of your accident, perhaps you damaged your watch, item of jewellery or clothing; you may be entitled to claim compensation for any personal damages.

Five:

By claiming against your employer for your accident, you are also preventing the accident from happening again, protecting others from being in the same situation as yourself. It may also result in your employer having to change procedures and processes in the workplace to reduce the likelihood of the accidents at work and the number of accident at work compensation claims being made.

If you’ve been injured or suffered any loss due to an accident in the workplace, seek the legal advice and guidance of a ‘No win, No Fee’ personal injury solicitor to see if you may be eligible to make an accident at work claim. Your solicitor will also access how much compensation you may be entitled to for any financial loss that you have suffered or medical treatment that you have had to pay out of your own pocket for.